{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "05_transfer_learning_in_tensorflow_part_2_fine_tuning_video",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMWD8xBIF6esvaYDRLt2v1c",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/video_notebooks/05_transfer_learning_in_tensorflow_part_2_fine_tuning_video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPY0bhH1QFpk"
      },
      "source": [
        "# Transfer Learning with TensorFlow Part 2: Fine-tuning\n",
        "\n",
        "In the previous notebook, we covered transfer learning feature extraction, now it's time to learn about a new kind of transfer learning: fine-tuning.\n",
        "\n",
        "See full course materials on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5l4FiZmQQVyc",
        "outputId": "8dc17e14-8aab-4bac-9422-35b6d3179026"
      },
      "source": [
        "# Check if we're using a GPU\n",
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu Feb 18 04:00:02 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.39       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wr2bTFf1Qc8S"
      },
      "source": [
        "## Creating helper functions\n",
        "\n",
        "In previous notebooks, we've created a bunch of helper functions, now we could rewrite them all, however, this is tedious.\n",
        "\n",
        "So, it's a good idea to put functions you'll want to use again in a script you can download and import into your notebooks (or elsewhere).\n",
        "\n",
        "We've done this for some of the functions we've used previously here: https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2WvnM0JwQpaH",
        "outputId": "1736de34-eb1d-4bfd-f969-79c8fe4a25d7"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-18 04:00:03--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9373 (9.2K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]   9.15K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-02-18 04:00:03 (117 MB/s) - ‘helper_functions.py’ saved [9373/9373]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxWn4EPERCd4"
      },
      "source": [
        "# Import helper functions we're going to use in this notebook\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_yHfw3VURjmR"
      },
      "source": [
        "> 🔑 **Note:** If you're running this notebook in Google Colab, when it times out Colab will delete `helper_functions.py`, so you'll have to redownload it if you want access to your helper functions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-I0ywFsRdCA"
      },
      "source": [
        "## Let's get some data\n",
        "\n",
        "This time we're going to see how we can use the pretrained models within `tf.keras.applications` and apply them to our own problem (recognizing images of food).\n",
        "\n",
        "link: https://www.tensorflow.org/api_docs/python/tf/keras/applications"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElqDi9yXTGn-",
        "outputId": "c7d822b4-ca9c-4110-9cf6-523231d94379"
      },
      "source": [
        "# Get 10% of training data of 10 classes of Food101\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
        "\n",
        "unzip_data(\"10_food_classes_10_percent.zip\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-18 04:00:05--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.8.16, 172.217.13.80, 172.217.13.240, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.8.16|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 168546183 (161M) [application/zip]\n",
            "Saving to: ‘10_food_classes_10_percent.zip’\n",
            "\n",
            "10_food_classes_10_ 100%[===================>] 160.74M   119MB/s    in 1.3s    \n",
            "\n",
            "2021-02-18 04:00:07 (119 MB/s) - ‘10_food_classes_10_percent.zip’ saved [168546183/168546183]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQJWvDXOTuhQ",
        "outputId": "3b54bdd5-e6cf-4656-ffe4-0ad1e694272f"
      },
      "source": [
        "# Check out how many images and subdirectories are in our dataset\n",
        "walk_through_dir(\"10_food_classes_10_percent\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_10_percent'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/train'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_curry'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/hamburger'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/sushi'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/grilled_salmon'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ice_cream'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/fried_rice'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ramen'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/pizza'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/steak'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_wings'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/steak'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_wings'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ni3iVJOPT_6-"
      },
      "source": [
        "# Create training and test directory paths\n",
        "train_dir = \"10_food_classes_10_percent/train\"\n",
        "test_dir = \"10_food_classes_10_percent/test\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwjKQ25WUUTs",
        "outputId": "285c0fc5-ce3c-477c-82b5-5159720dc4b9"
      },
      "source": [
        "import tensorflow as tf\n",
        "IMG_SIZE = (224, 224)\n",
        "BATCH_SIZE = 32\n",
        "train_data_10_percent = tf.keras.preprocessing.image_dataset_from_directory(directory=train_dir,\n",
        "                                                                            image_size=IMG_SIZE,\n",
        "                                                                            label_mode=\"categorical\",\n",
        "                                                                            batch_size=BATCH_SIZE)\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(directory=test_dir,\n",
        "                                                                image_size=IMG_SIZE,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 750 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HRZuiPNVcxb",
        "outputId": "8967ae7d-b1cb-4a1a-8cc8-57e3e0d7272f"
      },
      "source": [
        "train_data_10_percent"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BatchDataset shapes: ((None, 224, 224, 3), (None, 10)), types: (tf.float32, tf.float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOSfYf8zViVf",
        "outputId": "9b4f5387-0637-4ccf-a65a-2015f7109054"
      },
      "source": [
        "# Check out the class names of our dataset\n",
        "train_data_10_percent.class_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['chicken_curry',\n",
              " 'chicken_wings',\n",
              " 'fried_rice',\n",
              " 'grilled_salmon',\n",
              " 'hamburger',\n",
              " 'ice_cream',\n",
              " 'pizza',\n",
              " 'ramen',\n",
              " 'steak',\n",
              " 'sushi']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSAS5AdUWFJH",
        "outputId": "b43fc9d0-ae69-43c0-b1b8-1fe6885b4c5c"
      },
      "source": [
        "# See an example of a batch of data\n",
        "for images, labels in train_data_10_percent.take(1):\n",
        "  print(images, labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[[[2.02459183e+02 1.87459183e+02 1.56459183e+02]\n",
            "   [2.05214279e+02 1.90811218e+02 1.56617340e+02]\n",
            "   [2.05071426e+02 1.92071426e+02 1.57994888e+02]\n",
            "   ...\n",
            "   [6.77755661e+01 4.94847183e+01 5.42859316e+00]\n",
            "   [7.14489288e+01 4.88520012e+01 3.71423340e+00]\n",
            "   [7.50155411e+01 5.10155373e+01 3.84202361e+00]]\n",
            "\n",
            "  [[2.02877548e+02 1.87877548e+02 1.55683670e+02]\n",
            "   [2.01137756e+02 1.87132645e+02 1.50418365e+02]\n",
            "   [2.07841843e+02 1.94056122e+02 1.57841843e+02]\n",
            "   ...\n",
            "   [6.97857361e+01 5.12142639e+01 8.92857170e+00]\n",
            "   [7.18520126e+01 4.88571167e+01 6.56627369e+00]\n",
            "   [7.78829346e+01 5.38829346e+01 9.07674789e+00]]\n",
            "\n",
            "  [[2.00704086e+02 1.84704086e+02 1.50984695e+02]\n",
            "   [1.98729584e+02 1.84515305e+02 1.45229584e+02]\n",
            "   [2.04000000e+02 1.90494904e+02 1.48739792e+02]\n",
            "   ...\n",
            "   [6.96889038e+01 5.09031639e+01 1.09031649e+01]\n",
            "   [7.45713654e+01 5.25305405e+01 1.14437885e+01]\n",
            "   [7.37144623e+01 4.94236717e+01 7.36746597e+00]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[2.11994965e+02 1.93637756e+02 1.60637756e+02]\n",
            "   [2.07913284e+02 1.89556076e+02 1.56556076e+02]\n",
            "   [2.09596924e+02 1.90596924e+02 1.57596924e+02]\n",
            "   ...\n",
            "   [2.37382614e+02 2.20382614e+02 2.01954086e+02]\n",
            "   [2.35413223e+02 2.18413223e+02 2.02413223e+02]\n",
            "   [2.36852097e+02 2.19066360e+02 2.04637833e+02]]\n",
            "\n",
            "  [[2.09974487e+02 1.90974487e+02 1.57974487e+02]\n",
            "   [2.09137787e+02 1.90137787e+02 1.57137787e+02]\n",
            "   [2.06127579e+02 1.86913300e+02 1.53913300e+02]\n",
            "   ...\n",
            "   [2.34214264e+02 2.17214264e+02 1.98785736e+02]\n",
            "   [2.33214325e+02 2.16214325e+02 2.00214325e+02]\n",
            "   [2.36000000e+02 2.18000000e+02 2.04000000e+02]]\n",
            "\n",
            "  [[2.02856934e+02 1.83856934e+02 1.50856934e+02]\n",
            "   [2.04453842e+02 1.85453842e+02 1.52453842e+02]\n",
            "   [2.05938644e+02 1.86724365e+02 1.53724365e+02]\n",
            "   ...\n",
            "   [2.32004990e+02 2.15004990e+02 1.96576462e+02]\n",
            "   [2.34688751e+02 2.17688751e+02 2.01688751e+02]\n",
            "   [2.33800888e+02 2.15800888e+02 2.01800888e+02]]]\n",
            "\n",
            "\n",
            " [[[1.37229584e+02 5.82295914e+01 2.82295914e+01]\n",
            "   [1.40168365e+02 6.11683655e+01 3.05714283e+01]\n",
            "   [1.41219391e+02 6.04336739e+01 2.97908154e+01]\n",
            "   ...\n",
            "   [1.13637825e+02 5.50663567e+01 3.88520927e+01]\n",
            "   [1.13642860e+02 5.46428566e+01 3.86428566e+01]\n",
            "   [1.17714355e+02 5.57143555e+01 4.07143555e+01]]\n",
            "\n",
            "  [[1.40045914e+02 6.10459213e+01 3.10459175e+01]\n",
            "   [1.43647964e+02 6.26479607e+01 3.26479607e+01]\n",
            "   [1.47015305e+02 6.63724518e+01 3.35867348e+01]\n",
            "   ...\n",
            "   [1.10540779e+02 5.35407753e+01 3.65407753e+01]\n",
            "   [1.16071457e+02 5.70714569e+01 4.10714569e+01]\n",
            "   [1.18596909e+02 5.65969048e+01 4.15969048e+01]]\n",
            "\n",
            "  [[1.38280609e+02 5.77091827e+01 2.84948978e+01]\n",
            "   [1.42357147e+02 6.13571434e+01 3.13571434e+01]\n",
            "   [1.45806122e+02 6.27602043e+01 3.03316326e+01]\n",
            "   ...\n",
            "   [1.10168350e+02 5.33826141e+01 3.63826141e+01]\n",
            "   [1.14984734e+02 5.59847298e+01 3.99847298e+01]\n",
            "   [1.18214249e+02 5.68571091e+01 4.16428223e+01]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[1.31244873e+02 1.55244873e+02 7.92448730e+01]\n",
            "   [1.29413422e+02 1.56168549e+02 7.98113403e+01]\n",
            "   [1.27025452e+02 1.55025452e+02 8.02397156e+01]\n",
            "   ...\n",
            "   [1.62612473e+02 2.22494934e+02 1.43306381e+02]\n",
            "   [1.70045868e+02 2.18030548e+02 1.52489761e+02]\n",
            "   [2.03694305e+02 2.41832108e+02 1.83704453e+02]]\n",
            "\n",
            "  [[1.37887756e+02 1.63887756e+02 8.98877487e+01]\n",
            "   [1.25035767e+02 1.52969437e+02 7.59694443e+01]\n",
            "   [1.30086746e+02 1.61229630e+02 8.31581955e+01]\n",
            "   ...\n",
            "   [1.70637802e+02 2.26933655e+02 1.55551102e+02]\n",
            "   [1.64142883e+02 2.15219421e+02 1.46005096e+02]\n",
            "   [1.83306412e+02 2.33592178e+02 1.62280899e+02]]\n",
            "\n",
            "  [[1.32290924e+02 1.58290924e+02 8.42909317e+01]\n",
            "   [1.38693817e+02 1.67688812e+02 9.23571472e+01]\n",
            "   [1.29637848e+02 1.62790924e+02 8.37143936e+01]\n",
            "   ...\n",
            "   [1.80086914e+02 2.31097092e+02 1.67745071e+02]\n",
            "   [1.62591782e+02 2.16801086e+02 1.46112228e+02]\n",
            "   [1.73872421e+02 2.31586777e+02 1.52800888e+02]]]\n",
            "\n",
            "\n",
            " [[[0.00000000e+00 1.22672188e+00 2.26721972e-01]\n",
            "   [5.80358505e-02 2.05803585e+00 1.05803585e+00]\n",
            "   [1.00000000e+00 3.00000000e+00 2.00000000e+00]\n",
            "   ...\n",
            "   [5.23663330e+00 6.23663330e+00 1.02366333e+01]\n",
            "   [6.94198608e+00 7.94198608e+00 1.19419861e+01]\n",
            "   [8.00000000e+00 9.00000000e+00 1.30000000e+01]]\n",
            "\n",
            "  [[3.27487379e-01 2.32748723e+00 1.32748735e+00]\n",
            "   [1.05389047e+00 3.05389047e+00 2.05389047e+00]\n",
            "   [1.92857170e+00 3.92857170e+00 2.92857170e+00]\n",
            "   ...\n",
            "   [9.09377670e+00 1.00937767e+01 1.40937767e+01]\n",
            "   [9.92442799e+00 1.09244280e+01 1.49244280e+01]\n",
            "   [1.08571434e+01 1.18571434e+01 1.58571434e+01]]\n",
            "\n",
            "  [[1.49139035e+00 3.49139047e+00 2.49139047e+00]\n",
            "   [2.25988555e+00 4.25988531e+00 3.25988555e+00]\n",
            "   [3.00000000e+00 5.00000000e+00 4.00000000e+00]\n",
            "   ...\n",
            "   [1.21859264e+01 1.11859264e+01 1.61859264e+01]\n",
            "   [1.27857141e+01 1.17857141e+01 1.67857132e+01]\n",
            "   [1.36469984e+01 1.26469984e+01 1.76469994e+01]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[1.37867569e+02 9.32420349e+01 4.92335587e+01]\n",
            "   [1.50337479e+02 1.02694687e+02 5.75984955e+01]\n",
            "   [1.55026627e+02 1.04493835e+02 5.85577316e+01]\n",
            "   ...\n",
            "   [9.56987839e+01 8.38993683e+01 7.54991760e+01]\n",
            "   [9.07600021e+01 8.07600021e+01 7.17600021e+01]\n",
            "   [9.01429443e+01 8.09286804e+01 7.19286804e+01]]\n",
            "\n",
            "  [[1.01360710e+02 6.66927185e+01 3.49784851e+01]\n",
            "   [1.11872025e+02 7.31090088e+01 4.22653046e+01]\n",
            "   [1.25941795e+02 8.37007751e+01 4.96794624e+01]\n",
            "   ...\n",
            "   [8.33741150e+01 7.10840149e+01 6.28473816e+01]\n",
            "   [8.34286499e+01 7.34286499e+01 6.44286499e+01]\n",
            "   [8.05921783e+01 7.15921783e+01 6.25921822e+01]]\n",
            "\n",
            "  [[1.04709885e+02 7.71930313e+01 5.48448563e+01]\n",
            "   [1.05751663e+02 7.52245255e+01 5.08901939e+01]\n",
            "   [1.08320084e+02 7.24715881e+01 4.59132805e+01]\n",
            "   ...\n",
            "   [8.67638855e+01 7.44737854e+01 6.62371521e+01]\n",
            "   [8.20444946e+01 7.20444946e+01 6.30444946e+01]\n",
            "   [7.85249329e+01 6.95249329e+01 6.05249290e+01]]]\n",
            "\n",
            "\n",
            " ...\n",
            "\n",
            "\n",
            " [[[2.38928566e+01 1.48928576e+01 1.71785717e+01]\n",
            "   [2.08724480e+01 1.06785707e+01 1.42755098e+01]\n",
            "   [3.06734715e+01 1.34540834e+01 2.11122456e+01]\n",
            "   ...\n",
            "   [2.23484344e+02 1.95351685e+02 1.27423065e+02]\n",
            "   [1.99948975e+02 1.63780563e+02 9.32805328e+01]\n",
            "   [2.04127670e+02 1.60127594e+02 8.61836624e+01]]\n",
            "\n",
            "  [[5.84745064e+01 4.80255280e+01 5.57347107e+01]\n",
            "   [3.96938820e+01 2.49795952e+01 3.19744930e+01]\n",
            "   [1.18826530e+02 9.48265305e+01 1.00627548e+02]\n",
            "   ...\n",
            "   [1.97734467e+02 1.54377319e+02 9.15915604e+01]\n",
            "   [1.85290878e+02 1.37775558e+02 7.13520508e+01]\n",
            "   [1.86244690e+02 1.35770142e+02 6.39843559e+01]]\n",
            "\n",
            "  [[1.23989807e+02 1.12346947e+02 1.25489807e+02]\n",
            "   [2.73061066e+01 8.95406532e+00 1.70459003e+01]\n",
            "   [1.90913254e+02 1.57387741e+02 1.63127533e+02]\n",
            "   ...\n",
            "   [1.62285736e+02 1.04663345e+02 4.65970078e+01]\n",
            "   [1.73836761e+02 1.16867348e+02 5.59387512e+01]\n",
            "   [1.71469269e+02 1.12331528e+02 4.61834869e+01]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[2.51780640e+02 2.46780640e+02 2.42780640e+02]\n",
            "   [2.48372513e+02 2.43372513e+02 2.39372513e+02]\n",
            "   [2.48683746e+02 2.43683746e+02 2.39683746e+02]\n",
            "   ...\n",
            "   [1.57652378e+01 2.34285278e+01 9.02541065e+00]\n",
            "   [1.81683426e+01 2.78111343e+01 1.00254583e+01]\n",
            "   [2.81379166e+01 3.77807083e+01 1.74235611e+01]]\n",
            "\n",
            "  [[2.52948914e+02 2.47948914e+02 2.43948914e+02]\n",
            "   [2.47928513e+02 2.42928513e+02 2.38928513e+02]\n",
            "   [2.43387756e+02 2.38387756e+02 2.34387756e+02]\n",
            "   ...\n",
            "   [1.47652721e+01 2.07652721e+01 9.30606842e+00]\n",
            "   [1.26225595e+01 1.86888962e+01 4.55622149e+00]\n",
            "   [2.20663929e+01 2.90663929e+01 1.30663919e+01]]\n",
            "\n",
            "  [[2.47326630e+02 2.42326630e+02 2.38326630e+02]\n",
            "   [2.43760269e+02 2.38760269e+02 2.34760269e+02]\n",
            "   [2.47199127e+02 2.42199127e+02 2.38199127e+02]\n",
            "   ...\n",
            "   [1.49438248e+01 1.79438248e+01 9.09688473e+00]\n",
            "   [1.86478405e+01 2.23161812e+01 9.31116199e+00]\n",
            "   [2.00410404e+01 2.60410404e+01 1.20410404e+01]]]\n",
            "\n",
            "\n",
            " [[[2.29229584e+02 1.93515305e+02 1.37872452e+02]\n",
            "   [2.25806122e+02 1.90091827e+02 1.34448975e+02]\n",
            "   [2.26005112e+02 1.90647964e+02 1.32494904e+02]\n",
            "   ...\n",
            "   [2.28224472e+02 1.93224472e+02 1.11229614e+02]\n",
            "   [2.28357147e+02 1.93000000e+02 1.13071426e+02]\n",
            "   [2.27714279e+02 1.91714279e+02 1.14428574e+02]]\n",
            "\n",
            "  [[2.23357147e+02 1.87357147e+02 1.29357147e+02]\n",
            "   [2.23790817e+02 1.87790817e+02 1.29647964e+02]\n",
            "   [2.27984695e+02 1.91984695e+02 1.33443878e+02]\n",
            "   ...\n",
            "   [2.25127487e+02 1.88071365e+02 1.09841820e+02]\n",
            "   [2.22724503e+02 1.84724503e+02 1.09867363e+02]\n",
            "   [2.26500076e+02 1.88500076e+02 1.15642929e+02]]\n",
            "\n",
            "  [[2.27566330e+02 1.91566330e+02 1.31994888e+02]\n",
            "   [2.25928574e+02 1.89928574e+02 1.29928574e+02]\n",
            "   [2.27188782e+02 1.91188782e+02 1.29617355e+02]\n",
            "   ...\n",
            "   [2.24994904e+02 1.87209167e+02 1.11566376e+02]\n",
            "   [2.25959198e+02 1.87959198e+02 1.15071472e+02]\n",
            "   [2.28933716e+02 1.90933716e+02 1.19933716e+02]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[2.29270432e+02 2.02127609e+02 1.62056137e+02]\n",
            "   [2.23142929e+02 1.98357224e+02 1.57500046e+02]\n",
            "   [2.35714340e+02 2.10928635e+02 1.70071457e+02]\n",
            "   ...\n",
            "   [2.26188736e+02 2.04525497e+02 1.54357117e+02]\n",
            "   [2.21387711e+02 1.99816299e+02 1.49602005e+02]\n",
            "   [2.19000000e+02 1.97428589e+02 1.47214294e+02]]\n",
            "\n",
            "  [[2.41188965e+02 2.18117554e+02 1.74117554e+02]\n",
            "   [2.41224701e+02 2.20010468e+02 1.73224701e+02]\n",
            "   [2.43015350e+02 2.21801117e+02 1.75015350e+02]\n",
            "   ...\n",
            "   [2.19499908e+02 1.97612122e+02 1.47556015e+02]\n",
            "   [2.20061234e+02 1.98204056e+02 1.48132645e+02]\n",
            "   [2.19000000e+02 1.99000000e+02 1.48000000e+02]]\n",
            "\n",
            "  [[2.40275558e+02 2.23918411e+02 1.85418381e+02]\n",
            "   [2.39719437e+02 2.24005142e+02 1.83576553e+02]\n",
            "   [2.39295959e+02 2.23857178e+02 1.82658188e+02]\n",
            "   ...\n",
            "   [2.00571411e+02 1.77158188e+02 1.27295929e+02]\n",
            "   [2.11377563e+02 1.88734711e+02 1.38734711e+02]\n",
            "   [2.10484528e+02 1.88484528e+02 1.38484528e+02]]]\n",
            "\n",
            "\n",
            " [[[2.88571434e+01 2.78571434e+01 2.28571434e+01]\n",
            "   [2.90459194e+01 2.80459194e+01 2.30459194e+01]\n",
            "   [1.44795923e+01 1.34795923e+01 8.47959232e+00]\n",
            "   ...\n",
            "   [2.00000000e+00 2.00000000e+00 2.00000000e+00]\n",
            "   [3.00000000e+00 3.00000000e+00 3.00000000e+00]\n",
            "   [3.00000000e+00 3.00000000e+00 3.00000000e+00]]\n",
            "\n",
            "  [[3.05459175e+01 2.95459175e+01 2.45459175e+01]\n",
            "   [2.37397957e+01 2.27397957e+01 1.77397957e+01]\n",
            "   [1.00612249e+01 9.06122494e+00 4.06122494e+00]\n",
            "   ...\n",
            "   [2.00000000e+00 2.00000000e+00 2.00000000e+00]\n",
            "   [2.06633878e+00 2.06633878e+00 2.06633878e+00]\n",
            "   [2.95408416e+00 2.95408416e+00 2.95408416e+00]]\n",
            "\n",
            "  [[3.71020432e+01 3.61020432e+01 3.11020412e+01]\n",
            "   [2.68979588e+01 2.58979588e+01 2.08979588e+01]\n",
            "   [1.53112259e+01 1.43112259e+01 9.31122589e+00]\n",
            "   ...\n",
            "   [1.21428561e+00 1.21428561e+00 1.21428561e+00]\n",
            "   [2.00000000e+00 2.00000000e+00 2.00000000e+00]\n",
            "   [2.07653809e+00 2.07653809e+00 2.07653809e+00]]\n",
            "\n",
            "  ...\n",
            "\n",
            "  [[3.88571510e+01 3.98571510e+01 3.48571510e+01]\n",
            "   [4.10000000e+01 4.20000000e+01 3.70000000e+01]\n",
            "   [4.32142792e+01 4.42142792e+01 3.92142792e+01]\n",
            "   ...\n",
            "   [1.90382645e+02 2.10382645e+02 2.19382645e+02]\n",
            "   [1.89000000e+02 2.09000000e+02 2.18000000e+02]\n",
            "   [1.89214294e+02 2.09214294e+02 2.18214294e+02]]\n",
            "\n",
            "  [[3.96428566e+01 4.06428566e+01 3.56428566e+01]\n",
            "   [4.10000000e+01 4.20000000e+01 3.70000000e+01]\n",
            "   [4.24285736e+01 4.34285736e+01 3.84285736e+01]\n",
            "   ...\n",
            "   [1.89341782e+02 2.09341782e+02 2.18341782e+02]\n",
            "   [1.88071411e+02 2.08071411e+02 2.17071411e+02]\n",
            "   [1.89071411e+02 2.09071411e+02 2.18071411e+02]]\n",
            "\n",
            "  [[3.96428566e+01 4.06428566e+01 3.56428566e+01]\n",
            "   [4.10000000e+01 4.20000000e+01 3.70000000e+01]\n",
            "   [4.24285736e+01 4.34285736e+01 3.84285736e+01]\n",
            "   ...\n",
            "   [1.90576492e+02 2.10576492e+02 2.19576492e+02]\n",
            "   [1.88688782e+02 2.08688782e+02 2.17688782e+02]\n",
            "   [1.90000031e+02 2.10000031e+02 2.19000031e+02]]]], shape=(32, 224, 224, 3), dtype=float32) tf.Tensor(\n",
            "[[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]], shape=(32, 10), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-UZp1YQCWaF-"
      },
      "source": [
        "## Model 0: Building a transfer learning feature extraction model using the Keras Functional API\n",
        "\n",
        "The sequential API is straight-forward, it runs our layers in sequential order.\n",
        "\n",
        "But the functional API gives us more flexibility with our models - https://www.tensorflow.org/guide/keras/functional"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oo6CFVBwaSs0",
        "outputId": "6f0fff0e-0e16-4240-f062-d2f5192813c7"
      },
      "source": [
        "# 1. Create base model with tf.keras.applications\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "\n",
        "# 2. Freeze the base model (so the underlying pre-trained patterns aren't updated during training)\n",
        "base_model.trainable = False \n",
        "\n",
        "# 3. Create inputs into our model\n",
        "inputs = tf.keras.layers.Input(shape=(224, 224, 3), name=\"input_layer\")\n",
        "\n",
        "# 4. If using a model like ResNet50V2 you will need to normalize inputs (you don't have to for EfficientNet(s))\n",
        "# x = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)(inputs)\n",
        "\n",
        "# 5. Pass the inputs to the base_model\n",
        "x = base_model(inputs)\n",
        "print(f\"Shape after passing inputs through base model: {x.shape}\")\n",
        "\n",
        "# 6. Average pool the outputs of the base model (aggregate all the most important infromation, reduce number of computations)\n",
        "x = tf.keras.layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
        "print(f\"Shape after GlobalAveragePooling2D: {x.shape}\")\n",
        "\n",
        "# 7. Create the output activation layer\n",
        "outputs = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "\n",
        "# 8. Combine the inputs with the outputs into a model\n",
        "model_0 = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "# 9. Compile the model\n",
        "model_0.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# 10. Fit the model and save its history\n",
        "history_10_percent = model_0.fit(train_data_10_percent,\n",
        "                                 epochs=5,\n",
        "                                 steps_per_epoch=len(train_data_10_percent),\n",
        "                                 validation_data=test_data,\n",
        "                                 validation_steps=int(0.25 * len(test_data)),\n",
        "                                 callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                        experiment_name=\"10_percent_feature_extraction\")])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16711680/16705208 [==============================] - 0s 0us/step\n",
            "Shape after passing inputs through base model: (None, 7, 7, 1280)\n",
            "Shape after GlobalAveragePooling2D: (None, 1280)\n",
            "Saving TensorBoard log files to: transfer_learning/10_percent_feature_extraction/20210218-040017\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - 18s 302ms/step - loss: 2.0412 - accuracy: 0.3072 - val_loss: 1.2826 - val_accuracy: 0.7138\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - 6s 224ms/step - loss: 1.1607 - accuracy: 0.7588 - val_loss: 0.8955 - val_accuracy: 0.8092\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - 6s 226ms/step - loss: 0.8388 - accuracy: 0.8180 - val_loss: 0.7491 - val_accuracy: 0.8257\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - 6s 223ms/step - loss: 0.6596 - accuracy: 0.8662 - val_loss: 0.6578 - val_accuracy: 0.8322\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - 6s 223ms/step - loss: 0.5814 - accuracy: 0.8595 - val_loss: 0.6299 - val_accuracy: 0.8355\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_qBZXnbd_TR",
        "outputId": "fc61fee1-3e88-4a27-802d-2d7f26c091b2"
      },
      "source": [
        "# Evalaute on the full test dataset\n",
        "model_0.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 121ms/step - loss: 0.6042 - accuracy: 0.8416\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6041704416275024, 0.8416000008583069]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dw2LxkrseXme",
        "outputId": "88956cbc-f0a6-46df-df41-172f7b6e4f75"
      },
      "source": [
        "# Check the layers in our base model\n",
        "for layer_number, layer in enumerate(base_model.layers):\n",
        "  print(layer_number, layer.name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_1\n",
            "1 rescaling\n",
            "2 normalization\n",
            "3 stem_conv_pad\n",
            "4 stem_conv\n",
            "5 stem_bn\n",
            "6 stem_activation\n",
            "7 block1a_dwconv\n",
            "8 block1a_bn\n",
            "9 block1a_activation\n",
            "10 block1a_se_squeeze\n",
            "11 block1a_se_reshape\n",
            "12 block1a_se_reduce\n",
            "13 block1a_se_expand\n",
            "14 block1a_se_excite\n",
            "15 block1a_project_conv\n",
            "16 block1a_project_bn\n",
            "17 block2a_expand_conv\n",
            "18 block2a_expand_bn\n",
            "19 block2a_expand_activation\n",
            "20 block2a_dwconv_pad\n",
            "21 block2a_dwconv\n",
            "22 block2a_bn\n",
            "23 block2a_activation\n",
            "24 block2a_se_squeeze\n",
            "25 block2a_se_reshape\n",
            "26 block2a_se_reduce\n",
            "27 block2a_se_expand\n",
            "28 block2a_se_excite\n",
            "29 block2a_project_conv\n",
            "30 block2a_project_bn\n",
            "31 block2b_expand_conv\n",
            "32 block2b_expand_bn\n",
            "33 block2b_expand_activation\n",
            "34 block2b_dwconv\n",
            "35 block2b_bn\n",
            "36 block2b_activation\n",
            "37 block2b_se_squeeze\n",
            "38 block2b_se_reshape\n",
            "39 block2b_se_reduce\n",
            "40 block2b_se_expand\n",
            "41 block2b_se_excite\n",
            "42 block2b_project_conv\n",
            "43 block2b_project_bn\n",
            "44 block2b_drop\n",
            "45 block2b_add\n",
            "46 block3a_expand_conv\n",
            "47 block3a_expand_bn\n",
            "48 block3a_expand_activation\n",
            "49 block3a_dwconv_pad\n",
            "50 block3a_dwconv\n",
            "51 block3a_bn\n",
            "52 block3a_activation\n",
            "53 block3a_se_squeeze\n",
            "54 block3a_se_reshape\n",
            "55 block3a_se_reduce\n",
            "56 block3a_se_expand\n",
            "57 block3a_se_excite\n",
            "58 block3a_project_conv\n",
            "59 block3a_project_bn\n",
            "60 block3b_expand_conv\n",
            "61 block3b_expand_bn\n",
            "62 block3b_expand_activation\n",
            "63 block3b_dwconv\n",
            "64 block3b_bn\n",
            "65 block3b_activation\n",
            "66 block3b_se_squeeze\n",
            "67 block3b_se_reshape\n",
            "68 block3b_se_reduce\n",
            "69 block3b_se_expand\n",
            "70 block3b_se_excite\n",
            "71 block3b_project_conv\n",
            "72 block3b_project_bn\n",
            "73 block3b_drop\n",
            "74 block3b_add\n",
            "75 block4a_expand_conv\n",
            "76 block4a_expand_bn\n",
            "77 block4a_expand_activation\n",
            "78 block4a_dwconv_pad\n",
            "79 block4a_dwconv\n",
            "80 block4a_bn\n",
            "81 block4a_activation\n",
            "82 block4a_se_squeeze\n",
            "83 block4a_se_reshape\n",
            "84 block4a_se_reduce\n",
            "85 block4a_se_expand\n",
            "86 block4a_se_excite\n",
            "87 block4a_project_conv\n",
            "88 block4a_project_bn\n",
            "89 block4b_expand_conv\n",
            "90 block4b_expand_bn\n",
            "91 block4b_expand_activation\n",
            "92 block4b_dwconv\n",
            "93 block4b_bn\n",
            "94 block4b_activation\n",
            "95 block4b_se_squeeze\n",
            "96 block4b_se_reshape\n",
            "97 block4b_se_reduce\n",
            "98 block4b_se_expand\n",
            "99 block4b_se_excite\n",
            "100 block4b_project_conv\n",
            "101 block4b_project_bn\n",
            "102 block4b_drop\n",
            "103 block4b_add\n",
            "104 block4c_expand_conv\n",
            "105 block4c_expand_bn\n",
            "106 block4c_expand_activation\n",
            "107 block4c_dwconv\n",
            "108 block4c_bn\n",
            "109 block4c_activation\n",
            "110 block4c_se_squeeze\n",
            "111 block4c_se_reshape\n",
            "112 block4c_se_reduce\n",
            "113 block4c_se_expand\n",
            "114 block4c_se_excite\n",
            "115 block4c_project_conv\n",
            "116 block4c_project_bn\n",
            "117 block4c_drop\n",
            "118 block4c_add\n",
            "119 block5a_expand_conv\n",
            "120 block5a_expand_bn\n",
            "121 block5a_expand_activation\n",
            "122 block5a_dwconv\n",
            "123 block5a_bn\n",
            "124 block5a_activation\n",
            "125 block5a_se_squeeze\n",
            "126 block5a_se_reshape\n",
            "127 block5a_se_reduce\n",
            "128 block5a_se_expand\n",
            "129 block5a_se_excite\n",
            "130 block5a_project_conv\n",
            "131 block5a_project_bn\n",
            "132 block5b_expand_conv\n",
            "133 block5b_expand_bn\n",
            "134 block5b_expand_activation\n",
            "135 block5b_dwconv\n",
            "136 block5b_bn\n",
            "137 block5b_activation\n",
            "138 block5b_se_squeeze\n",
            "139 block5b_se_reshape\n",
            "140 block5b_se_reduce\n",
            "141 block5b_se_expand\n",
            "142 block5b_se_excite\n",
            "143 block5b_project_conv\n",
            "144 block5b_project_bn\n",
            "145 block5b_drop\n",
            "146 block5b_add\n",
            "147 block5c_expand_conv\n",
            "148 block5c_expand_bn\n",
            "149 block5c_expand_activation\n",
            "150 block5c_dwconv\n",
            "151 block5c_bn\n",
            "152 block5c_activation\n",
            "153 block5c_se_squeeze\n",
            "154 block5c_se_reshape\n",
            "155 block5c_se_reduce\n",
            "156 block5c_se_expand\n",
            "157 block5c_se_excite\n",
            "158 block5c_project_conv\n",
            "159 block5c_project_bn\n",
            "160 block5c_drop\n",
            "161 block5c_add\n",
            "162 block6a_expand_conv\n",
            "163 block6a_expand_bn\n",
            "164 block6a_expand_activation\n",
            "165 block6a_dwconv_pad\n",
            "166 block6a_dwconv\n",
            "167 block6a_bn\n",
            "168 block6a_activation\n",
            "169 block6a_se_squeeze\n",
            "170 block6a_se_reshape\n",
            "171 block6a_se_reduce\n",
            "172 block6a_se_expand\n",
            "173 block6a_se_excite\n",
            "174 block6a_project_conv\n",
            "175 block6a_project_bn\n",
            "176 block6b_expand_conv\n",
            "177 block6b_expand_bn\n",
            "178 block6b_expand_activation\n",
            "179 block6b_dwconv\n",
            "180 block6b_bn\n",
            "181 block6b_activation\n",
            "182 block6b_se_squeeze\n",
            "183 block6b_se_reshape\n",
            "184 block6b_se_reduce\n",
            "185 block6b_se_expand\n",
            "186 block6b_se_excite\n",
            "187 block6b_project_conv\n",
            "188 block6b_project_bn\n",
            "189 block6b_drop\n",
            "190 block6b_add\n",
            "191 block6c_expand_conv\n",
            "192 block6c_expand_bn\n",
            "193 block6c_expand_activation\n",
            "194 block6c_dwconv\n",
            "195 block6c_bn\n",
            "196 block6c_activation\n",
            "197 block6c_se_squeeze\n",
            "198 block6c_se_reshape\n",
            "199 block6c_se_reduce\n",
            "200 block6c_se_expand\n",
            "201 block6c_se_excite\n",
            "202 block6c_project_conv\n",
            "203 block6c_project_bn\n",
            "204 block6c_drop\n",
            "205 block6c_add\n",
            "206 block6d_expand_conv\n",
            "207 block6d_expand_bn\n",
            "208 block6d_expand_activation\n",
            "209 block6d_dwconv\n",
            "210 block6d_bn\n",
            "211 block6d_activation\n",
            "212 block6d_se_squeeze\n",
            "213 block6d_se_reshape\n",
            "214 block6d_se_reduce\n",
            "215 block6d_se_expand\n",
            "216 block6d_se_excite\n",
            "217 block6d_project_conv\n",
            "218 block6d_project_bn\n",
            "219 block6d_drop\n",
            "220 block6d_add\n",
            "221 block7a_expand_conv\n",
            "222 block7a_expand_bn\n",
            "223 block7a_expand_activation\n",
            "224 block7a_dwconv\n",
            "225 block7a_bn\n",
            "226 block7a_activation\n",
            "227 block7a_se_squeeze\n",
            "228 block7a_se_reshape\n",
            "229 block7a_se_reduce\n",
            "230 block7a_se_expand\n",
            "231 block7a_se_excite\n",
            "232 block7a_project_conv\n",
            "233 block7a_project_bn\n",
            "234 top_conv\n",
            "235 top_bn\n",
            "236 top_activation\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ww2gKirGen-C",
        "outputId": "0f1bb2c4-d712-4fb7-d1a8-abdd159eb137"
      },
      "source": [
        "# How about we get a summary of the base model?\n",
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"efficientnetb0\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling (Rescaling)           (None, None, None, 3 0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization (Normalization)   (None, None, None, 3 7           rescaling[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, None, None, 3 0           normalization[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv (Conv2D)              (None, None, None, 3 864         stem_conv_pad[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_bn (BatchNormalization)    (None, None, None, 3 128         stem_conv[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_activation (Activation)    (None, None, None, 3 0           stem_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1a_dwconv (DepthwiseConv2D (None, None, None, 3 288         stem_activation[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1a_bn (BatchNormalization) (None, None, None, 3 128         block1a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block1a_activation (Activation) (None, None, None, 3 0           block1a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_excite (Multiply)    (None, None, None, 3 0           block1a_activation[0][0]         \n",
            "                                                                 block1a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_conv (Conv2D)   (None, None, None, 1 512         block1a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_bn (BatchNormal (None, None, None, 1 64          block1a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_conv (Conv2D)    (None, None, None, 9 1536        block1a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_bn (BatchNormali (None, None, None, 9 384         block2a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_activation (Acti (None, None, None, 9 0           block2a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv_pad (ZeroPadding (None, None, None, 9 0           block2a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv (DepthwiseConv2D (None, None, None, 9 864         block2a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_bn (BatchNormalization) (None, None, None, 9 384         block2a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2a_activation (Activation) (None, None, None, 9 0           block2a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_excite (Multiply)    (None, None, None, 9 0           block2a_activation[0][0]         \n",
            "                                                                 block2a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_conv (Conv2D)   (None, None, None, 2 2304        block2a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_bn (BatchNormal (None, None, None, 2 96          block2a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_conv (Conv2D)    (None, None, None, 1 3456        block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_bn (BatchNormali (None, None, None, 1 576         block2b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_activation (Acti (None, None, None, 1 0           block2b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_dwconv (DepthwiseConv2D (None, None, None, 1 1296        block2b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2b_bn (BatchNormalization) (None, None, None, 1 576         block2b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2b_activation (Activation) (None, None, None, 1 0           block2b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_excite (Multiply)    (None, None, None, 1 0           block2b_activation[0][0]         \n",
            "                                                                 block2b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_conv (Conv2D)   (None, None, None, 2 3456        block2b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_bn (BatchNormal (None, None, None, 2 96          block2b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_drop (Dropout)          (None, None, None, 2 0           block2b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_add (Add)               (None, None, None, 2 0           block2b_drop[0][0]               \n",
            "                                                                 block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_conv (Conv2D)    (None, None, None, 1 3456        block2b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_bn (BatchNormali (None, None, None, 1 576         block3a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_activation (Acti (None, None, None, 1 0           block3a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv_pad (ZeroPadding (None, None, None, 1 0           block3a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv (DepthwiseConv2D (None, None, None, 1 3600        block3a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_bn (BatchNormalization) (None, None, None, 1 576         block3a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3a_activation (Activation) (None, None, None, 1 0           block3a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_excite (Multiply)    (None, None, None, 1 0           block3a_activation[0][0]         \n",
            "                                                                 block3a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_conv (Conv2D)   (None, None, None, 4 5760        block3a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_bn (BatchNormal (None, None, None, 4 160         block3a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_conv (Conv2D)    (None, None, None, 2 9600        block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_bn (BatchNormali (None, None, None, 2 960         block3b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_activation (Acti (None, None, None, 2 0           block3b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_dwconv (DepthwiseConv2D (None, None, None, 2 6000        block3b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3b_bn (BatchNormalization) (None, None, None, 2 960         block3b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3b_activation (Activation) (None, None, None, 2 0           block3b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_excite (Multiply)    (None, None, None, 2 0           block3b_activation[0][0]         \n",
            "                                                                 block3b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_conv (Conv2D)   (None, None, None, 4 9600        block3b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_bn (BatchNormal (None, None, None, 4 160         block3b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_drop (Dropout)          (None, None, None, 4 0           block3b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_add (Add)               (None, None, None, 4 0           block3b_drop[0][0]               \n",
            "                                                                 block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_conv (Conv2D)    (None, None, None, 2 9600        block3b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_bn (BatchNormali (None, None, None, 2 960         block4a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_activation (Acti (None, None, None, 2 0           block4a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv_pad (ZeroPadding (None, None, None, 2 0           block4a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv (DepthwiseConv2D (None, None, None, 2 2160        block4a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_bn (BatchNormalization) (None, None, None, 2 960         block4a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4a_activation (Activation) (None, None, None, 2 0           block4a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_excite (Multiply)    (None, None, None, 2 0           block4a_activation[0][0]         \n",
            "                                                                 block4a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_conv (Conv2D)   (None, None, None, 8 19200       block4a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_bn (BatchNormal (None, None, None, 8 320         block4a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_conv (Conv2D)    (None, None, None, 4 38400       block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_bn (BatchNormali (None, None, None, 4 1920        block4b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_activation (Acti (None, None, None, 4 0           block4b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_dwconv (DepthwiseConv2D (None, None, None, 4 4320        block4b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4b_bn (BatchNormalization) (None, None, None, 4 1920        block4b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4b_activation (Activation) (None, None, None, 4 0           block4b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_excite (Multiply)    (None, None, None, 4 0           block4b_activation[0][0]         \n",
            "                                                                 block4b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_conv (Conv2D)   (None, None, None, 8 38400       block4b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_bn (BatchNormal (None, None, None, 8 320         block4b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_drop (Dropout)          (None, None, None, 8 0           block4b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_add (Add)               (None, None, None, 8 0           block4b_drop[0][0]               \n",
            "                                                                 block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_conv (Conv2D)    (None, None, None, 4 38400       block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_bn (BatchNormali (None, None, None, 4 1920        block4c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_activation (Acti (None, None, None, 4 0           block4c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_dwconv (DepthwiseConv2D (None, None, None, 4 4320        block4c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4c_bn (BatchNormalization) (None, None, None, 4 1920        block4c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4c_activation (Activation) (None, None, None, 4 0           block4c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_excite (Multiply)    (None, None, None, 4 0           block4c_activation[0][0]         \n",
            "                                                                 block4c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_conv (Conv2D)   (None, None, None, 8 38400       block4c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_bn (BatchNormal (None, None, None, 8 320         block4c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4c_drop (Dropout)          (None, None, None, 8 0           block4c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_add (Add)               (None, None, None, 8 0           block4c_drop[0][0]               \n",
            "                                                                 block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_conv (Conv2D)    (None, None, None, 4 38400       block4c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_bn (BatchNormali (None, None, None, 4 1920        block5a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_activation (Acti (None, None, None, 4 0           block5a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_dwconv (DepthwiseConv2D (None, None, None, 4 12000       block5a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5a_bn (BatchNormalization) (None, None, None, 4 1920        block5a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5a_activation (Activation) (None, None, None, 4 0           block5a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_excite (Multiply)    (None, None, None, 4 0           block5a_activation[0][0]         \n",
            "                                                                 block5a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_conv (Conv2D)   (None, None, None, 1 53760       block5a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_bn (BatchNormal (None, None, None, 1 448         block5a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_conv (Conv2D)    (None, None, None, 6 75264       block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_bn (BatchNormali (None, None, None, 6 2688        block5b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_activation (Acti (None, None, None, 6 0           block5b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_dwconv (DepthwiseConv2D (None, None, None, 6 16800       block5b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5b_bn (BatchNormalization) (None, None, None, 6 2688        block5b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5b_activation (Activation) (None, None, None, 6 0           block5b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_excite (Multiply)    (None, None, None, 6 0           block5b_activation[0][0]         \n",
            "                                                                 block5b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_conv (Conv2D)   (None, None, None, 1 75264       block5b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_bn (BatchNormal (None, None, None, 1 448         block5b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_drop (Dropout)          (None, None, None, 1 0           block5b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_add (Add)               (None, None, None, 1 0           block5b_drop[0][0]               \n",
            "                                                                 block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_conv (Conv2D)    (None, None, None, 6 75264       block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_bn (BatchNormali (None, None, None, 6 2688        block5c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_activation (Acti (None, None, None, 6 0           block5c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_dwconv (DepthwiseConv2D (None, None, None, 6 16800       block5c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5c_bn (BatchNormalization) (None, None, None, 6 2688        block5c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5c_activation (Activation) (None, None, None, 6 0           block5c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_excite (Multiply)    (None, None, None, 6 0           block5c_activation[0][0]         \n",
            "                                                                 block5c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_conv (Conv2D)   (None, None, None, 1 75264       block5c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_bn (BatchNormal (None, None, None, 1 448         block5c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5c_drop (Dropout)          (None, None, None, 1 0           block5c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_add (Add)               (None, None, None, 1 0           block5c_drop[0][0]               \n",
            "                                                                 block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_conv (Conv2D)    (None, None, None, 6 75264       block5c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_bn (BatchNormali (None, None, None, 6 2688        block6a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_activation (Acti (None, None, None, 6 0           block6a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv_pad (ZeroPadding (None, None, None, 6 0           block6a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv (DepthwiseConv2D (None, None, None, 6 16800       block6a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_bn (BatchNormalization) (None, None, None, 6 2688        block6a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6a_activation (Activation) (None, None, None, 6 0           block6a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_excite (Multiply)    (None, None, None, 6 0           block6a_activation[0][0]         \n",
            "                                                                 block6a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_conv (Conv2D)   (None, None, None, 1 129024      block6a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_bn (BatchNormal (None, None, None, 1 768         block6a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_conv (Conv2D)    (None, None, None, 1 221184      block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_bn (BatchNormali (None, None, None, 1 4608        block6b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_activation (Acti (None, None, None, 1 0           block6b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_dwconv (DepthwiseConv2D (None, None, None, 1 28800       block6b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6b_bn (BatchNormalization) (None, None, None, 1 4608        block6b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6b_activation (Activation) (None, None, None, 1 0           block6b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_excite (Multiply)    (None, None, None, 1 0           block6b_activation[0][0]         \n",
            "                                                                 block6b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_conv (Conv2D)   (None, None, None, 1 221184      block6b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_bn (BatchNormal (None, None, None, 1 768         block6b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_drop (Dropout)          (None, None, None, 1 0           block6b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_add (Add)               (None, None, None, 1 0           block6b_drop[0][0]               \n",
            "                                                                 block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_conv (Conv2D)    (None, None, None, 1 221184      block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_bn (BatchNormali (None, None, None, 1 4608        block6c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_activation (Acti (None, None, None, 1 0           block6c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_dwconv (DepthwiseConv2D (None, None, None, 1 28800       block6c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6c_bn (BatchNormalization) (None, None, None, 1 4608        block6c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6c_activation (Activation) (None, None, None, 1 0           block6c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_excite (Multiply)    (None, None, None, 1 0           block6c_activation[0][0]         \n",
            "                                                                 block6c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_conv (Conv2D)   (None, None, None, 1 221184      block6c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_bn (BatchNormal (None, None, None, 1 768         block6c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6c_drop (Dropout)          (None, None, None, 1 0           block6c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_add (Add)               (None, None, None, 1 0           block6c_drop[0][0]               \n",
            "                                                                 block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_conv (Conv2D)    (None, None, None, 1 221184      block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_bn (BatchNormali (None, None, None, 1 4608        block6d_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_activation (Acti (None, None, None, 1 0           block6d_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_dwconv (DepthwiseConv2D (None, None, None, 1 28800       block6d_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6d_bn (BatchNormalization) (None, None, None, 1 4608        block6d_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6d_activation (Activation) (None, None, None, 1 0           block6d_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_excite (Multiply)    (None, None, None, 1 0           block6d_activation[0][0]         \n",
            "                                                                 block6d_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_conv (Conv2D)   (None, None, None, 1 221184      block6d_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_bn (BatchNormal (None, None, None, 1 768         block6d_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6d_drop (Dropout)          (None, None, None, 1 0           block6d_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_add (Add)               (None, None, None, 1 0           block6d_drop[0][0]               \n",
            "                                                                 block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_conv (Conv2D)    (None, None, None, 1 221184      block6d_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_bn (BatchNormali (None, None, None, 1 4608        block7a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_activation (Acti (None, None, None, 1 0           block7a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_dwconv (DepthwiseConv2D (None, None, None, 1 10368       block7a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block7a_bn (BatchNormalization) (None, None, None, 1 4608        block7a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block7a_activation (Activation) (None, None, None, 1 0           block7a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_excite (Multiply)    (None, None, None, 1 0           block7a_activation[0][0]         \n",
            "                                                                 block7a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_conv (Conv2D)   (None, None, None, 3 368640      block7a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_bn (BatchNormal (None, None, None, 3 1280        block7a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "top_conv (Conv2D)               (None, None, None, 1 409600      block7a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "top_bn (BatchNormalization)     (None, None, None, 1 5120        top_conv[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "top_activation (Activation)     (None, None, None, 1 0           top_bn[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 4,049,571\n",
            "Trainable params: 0\n",
            "Non-trainable params: 4,049,571\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgpnw0mLe48M",
        "outputId": "01711c3d-5f28-46e8-8839-43acbafc259f"
      },
      "source": [
        "# How about a summary of our whole model?\n",
        "model_0.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (InputLayer)     [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "efficientnetb0 (Functional)  (None, None, None, 1280)  4049571   \n",
            "_________________________________________________________________\n",
            "global_average_pooling_layer (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 10)                12810     \n",
            "=================================================================\n",
            "Total params: 4,062,381\n",
            "Trainable params: 12,810\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "LLEd5WgpfH0g",
        "outputId": "fc6757b5-3e6f-455f-a6f5-d269eff3777b"
      },
      "source": [
        "# Check out our model's training curves\n",
        "plot_loss_curves(history_10_percent)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1UW1uFdkqkKdG9yNjdwGvf7fN7nKUUqrRXNFD7wFEisg3IrJSRH5d20ARmSAiaSKS5oln0Ecmt+OKfvH8fVEWm3OL7S5HKaUaxRWBHgAMBEYBlwOPikiPmgYaY6YZY1KNMakxMZ65JvmTo/sSGujP5JnpVFbpdbhKKe/hikDPBuYbYw4bY/YDS4H+LtivLWLDg3n8qj6s2nmIGd9vt7scpZRqMFcE+mfA+SISICKhwC+ADS7Yr23GDOjAJb1imTo/kx35h+0uRymlGqQhly2+DywHeopItojcISJ3i8jdAMaYDcBXwBrgJ+ANY0ytlzh6AxHhmWv64fDz48FP1lClrRellBeod6aoMeaGBoyZCkx1SUUeol2bEB4e1ZspszJ476ed3DS4s90lKaVUnVr8TNG6jD+nI+d3j+bZuRvYfeio3eUopVSdNNDrICI8OzYZAzw0K0NXn1NKeTQN9Hp0bBvKlCt6sTQrj5krs+0uRymlaqWB3gA3/aIzgxLb8ucv1pNTWGJ3OUopVSMN9Abw8xOeH5dCaUUVD89eq60XpZRH0kBvoC7Rrfjj8J4s2pDDnPQ9dpejlFKn0UBvhNvP70L/jhE8MWcd+4tL7S5HKaVOooHeCP5+wtRxKRwureTxOevsLkcppU6igd5IPeLCuffS7ny5Zi9frd1rdzlKKXWcBvoZ+J+LutG3fWse+XQdh46U2V2OUkoBGuhnxOHvxwvjUjh0pIynvlhvdzlKKQVooJ+xvu3b8Juh3Zi1ajdLMr3yJk1KKR+jgd4E91zSnR5xYfxpdgaFJeV2l6OUauE00JsgKMCfF8b1J6ewhGfnevUS8EopH6CB3kQDOkZw1wVdef+nXSzbtN/ucpRSLZgGugvcP6wHXaJbMWXWGg6XVthdjlKqhdJAd4Fghz8vjEth96GjTJ2/0e5ylFItlAa6i5yT2JZbhiQy4/vt/LTtgN3lKKVaIA10F5o8oicd24bw4CdrKCmvtLscpVQLo4HuQqGBATw3NoVt+w/z94VZdpejlGphNNBd7Lzu0dwwqBOvf7eV1bsO2V2OUqoF0UBvBg+N7EVc62Amz0yntEJbL0op99BAbwatgx387zXJZOUU8/LizXaXo5RqIeoNdBGZLiK5IrK2nnHniEiFiIxzXXne6+JesYw9uwOvfLOFtbsL7C5HKdUCNOQIfQYwoq4BIuIPPA8scEFNPuOxK/sQGRrI5JlrKK+ssrscpZSPqzfQjTFLgfourP4d8Amgyw5WExEayNNj+rF+byH/+naL3eUopXxck3voItIBuAZ4tQFjJ4hImoik5eXlNfWpvcKIfvGMSmnHS19vJiunyO5ylFI+zBUnRf8BPGiMqbenYIyZZoxJNcakxsTEuOCpvcNTV/clLDiASTPXUFll7C5HKeWjXBHoqcAHIrIdGAe8IiJjXLBfnxEVFsQTV/clfdchpi/bZnc5Sikf1eRAN8Z0McYkGmMSgZnAb4wxnza5Mh9zVUo7hvWJ4y8LNrJt/2G7y1FK+aCGXLb4PrAc6Cki2SJyh4jcLSJ3N395vkNEeHpMP4IC/Hhw5hqqtPWilHKxgPoGGGNuaOjOjDG3NqkaHxfXOphHr+zDpJlreOfHHfx6SKLdJSmlfIjOFHWzcQMTuLBHDM/Ny2TXgSN2l6OU8iEa6G4mIjw7NhkBpsxagzHaelFKuYYGug06RITw0Mje/HdzPh+u2GV3OUopH6GBbpMbB3VicNe2PPPlBvYWHLW7HKWUD9BAt4mfn/D8tSlUVBn+NCtDWy9KqSbTQLdR56hWTLq8J0s25vHp6t12l6OU8nIa6Da75dxEBnaO5Ik568ktKrG7HKWUF9NAt5m/s/VytLySxz9bZ3c5SikvpoHuAbrHhnH/ZT2Yt3YfczP22l2OUspLaaB7iLsu6EJyhzY8+ulaDhwus7scpZQX0kD3EAH+fky9LoXCknKe/FxbL0qpxtNA9yC94ltzz8Xd+Wz1Hhauz7G7HKWUl/G+QM/LghlXQkG23ZU0i98M7U6v+HAenp1BwdFyu8tRSnkR7wv0oj2wNx2mDYWdP9hdjcsFBvgxdVx/8g+X8cyX6+0uRynlRbwv0LsOhTsXQVC4daS+8k27K3K55IQ2/M+FXfkoLZulWS3j3qtKqabzvkAHiOkJdy2GLhfA5/fC3MlQ6VvtiXsvTaJbTCsempVBcWmF3eUopbyAdwY6QEgk3PgxDPkt/PQveGcsHDlgd1UuE+zw54Vx/dlTcJTn52XaXY5Sygt4b6AD+AfA5c/AmNesfvrrF0OO7/SdB3aO5PbzuvD2Dzv4YWu+3eUopTycdwf6MQNugFvnQvlR+PcwyPzS7opc5o/De9I5KpQHP1nD0bJKu8tRSnkw3wh0gI7nwIRvILoHfHAjfDsVfGBJ2pBAf54bm8KO/CP8ZcFGu8tRSnkw3wl0gNbt4ba5kDIeljwNH98KZYftrqrJhnSL4qbBnZj+322s3HHQ7nKUUh7KtwIdwBEC1/wLhv0ZNsyB6ZfDoZ12V9VkU67oTfs2IUyemU5JubZelFKn871ABxCB8+6FGz+Cgzth2sWw43u7q2qSsKAAnh2bzJa8w7z09Sa7y1FKeaB6A11EpotIroisrWX7r0RkjYhkiMj3ItLf9WWeoaRhcNfXEBIBb14Faf+xu6ImubBHDNenJvCvpVvJyC6wuxyllIdpyBH6DGBEHdu3ARcZY5KBPwPTXFCX60QnwZ1fWzNMv/g9fPmAV09CenhUH6JaBTJpZjplFVV2l6OU8iD1BroxZilQ64wdY8z3xphjZ+p+ABJcVJvrhERY7Zdz74UVb8Db18Bh77yuu02Ig2euSSZzXxGvfrPF7nKUUh7E1T30O4B5tW0UkQkikiYiaXl5bl6jxM8fhv8ZrpkGu36C14fCvhq7SB5vWJ84Rg9oz8tLNpG5r9DucpRSHsJlgS4iF2MF+oO1jTHGTDPGpBpjUmNiYlz11I3TfzzcNs9qu/x7OKyfY08dTfT4VX1pHexg0sdrqKjU1otSykWBLiIpwBvAaGOM5/cyEgZak5Bie8NHN8M3z0GVd4Vi21aBPDW6Hxm7C3j9u212l6OU8gBNDnQR6QTMAm42xmQ1vSQ3CY+HW7+E/jfCN8/Cx7dAabHdVTXKyOR4RvSN5++Lstic6121K6VcryGXLb4PLAd6iki2iNwhIneLyN3OIY8BUcArIrJaRNKasV7XcgTDmFfg8v+FzC+sSUgHd9hdVYOJCE+N6UtooD+TZ6ZTWeX9Sx0opc6cGJvWO0lNTTVpaR6U/ZsXwczbwS8Arn8LEs+3u6IGm/1zNvd/mM5jV/bh9vO72F2OUqoZichKY0xqTdt8c6bomeh+Gdy1BEKj4K3R1uWNXmLMgA5c0iuWF+ZnsiPf+9euUUqdGQ306qK6Wbe363apNQHp899DRZndVdVLRHjmmn44/PyY8kkGVdp6UapF0kA/VXAbuOF9OO/3sPI/1tH64f12V1Wvdm1CeHhUb5Zvzef9Fd6/GJlSqvE00Gvi5w/DnoSxb8CeVdbiXvsy7K6qXuPP6ch53aN4dm4muw8dtbscpZSbaaDXJeU6axJSVYU1CWndp3ZXVCcR4bmxKVQZw0OzMrDrhLdSyh4a6PXpcLY1CSmun3Wt+uJnPHoSUse2oTw4ohdLs/KYuTLb7nKUUm6kgd4Q4XFw6xcw4CZY+oI1u7S0yO6qanXz4M4MSmzLn79YT05hid3lKKXcRAO9oQKCYPTLMOI52DjPasEc8Mwp935+wvPjUiitqOLh2Wu19aJUC6GB3hgiMHgi3PQJFO6B1y+GbUvtrqpGXaJb8cfhPVm0IYfP1+y1uxyllBtooJ+JbhfDXYshLA7eGgM/TgMPPAq+/fwu9O8YweOfrWXDXl1mVylfp4F+pqK6wR0LIWk4zJsEn9/rcZOQ/P2Ev16Xgohw5f8t44k56ygs8d67NSml6qaB3hTBreGX78EFD8Cqt+Ctq6HYzTfuqEf32HAWP3ARNwzqyJvLt3PJX75l1qps7asr5YM00JvKzw8ufQzGTYc9q2HaUNibbndVJ4kIDeTpMcnMued8EiJD+MNH6Vz/r+XahlHKx2igu0q/a+H2r6zP/305rP3E3npqkJzQhlkTz+X5a5PZnFusbRilfIwGuiu1HwATlkC7/tZSvF//2eMmIfn5CePP6cSSPw7VNoxSPkYD3dXCYuGWOXDWzfDdX+DDX0GJ57U2tA2jlO/RQG8OAUFw9f/BFVMhaz78exgc2Gp3VTXSNoxSvkMDvbmIwC8mwM2zoTjHWrFxyxK7q6qRtmGU8g0a6M2t60XWnZDC28E718IPr3nkJCTQNoxS3k4D3R3adoE7F0KPEfDVgzDnt1BRandVtdI2jFLeSQPdXYLCYfw7cOFk+PkdePMqKMqxu6paaRtGKe+jge5Ofn5wycNw3QzrDkivXwx7fra7qjppG0Yp76GBboe+18Dt80H8YPoIyJhpd0X1qqkN8+Tn2oZRypPUG+giMl1EckVkbS3bRUReEpHNIrJGRM52fZk+qF2KdbK0/dnwyR2w6AmoqrS7qjqd2oaZ8b22YZTyJA05Qp8BjKhj+xVAkvNjAvBq08tqIcJi4NefwcBbYdnf4f0bPHIS0qm0DaOUZ6o30I0xS4EDdQwZDbxlLD8AESLSzlUF+ryAQLjqRRj1V9jyNbxxGeRvsbuqBtE2jFKexRU99A7ArmpfZzsfO42ITBCRNBFJy8vzrGVmbXfOnXDzp3A4zzpZuvlruytqEG3DKOU53HpS1BgzzRiTaoxJjYmJcedTe4cuF1iLe7VOgHfHwfJ/euwkpFMda8N8ds95dNA2jFK2cEWg7wY6Vvs6wfmYOhORiXDHAug5Eub/CT67x6MnIZ0qJSGC2dqGUcoWrgj0OcCvnVe7DAYKjDF6V+KmCAqD69+GoQ/B6ndhxigo2md3VQ2mbRil7CH1vcFE5H1gKBAN5ACPAw4AY8xrIiLAy1hXwhwBbjPGpNX3xKmpqSYtrd5hav1nMPtuCG4Dv3wXOgy0u6JGW5N9iEc/W0f6rkOckxjJU6P70btda7vLUsorichKY0xqjdvsOmLSQG+EfRnw/o3Wqo2jX4aU6+2uqNGqqgwfr9zFc/MyKSyp4NdDOnP/sB60DnbYXZpSXqWuQNeZot4gPtk6WZpwDsy6CxY+5vGTkE6lbRilmp8GurdoFQ2//tS6vPG/L8J74+HoIburajS9Gkap5qOB7k38HdYEpCv/DluXWJOQ9m+2u6ozolfDKOV6GujeKPV2+PUcOHoAXr8ENi2yu6Izom0YpVxLA91bJZ5nLe4V0Qneuw6+/z+vmYR0Km3DKOUaGujeLLIz3DEfel8FCx6xLm8sL7G7qjOmbRilmkYD3dsFtoLr3oSLH4Y1H8CMkVDovfO6tA2j1JnTQPcFInDRZOsWd7mZMG0oZHv3Nf7ahlGq8TTQfUnvq6ybUQcEwX9Gwur37a6oybQNo1TDaaD7mri+MOEb6DgIPr0b5j8MlRV2V9Uk2oZRqmE00H1RaFu4eTYMmgDLX7augtmx3Otml55K2zBK1U3XcvF1K2fA3MlQWQqh0dBjBPQaCV0vhsBQu6s7Y7o2jGqpdHGulq6kADYvgsy5sGkhlBZAQAh0u8QK9x4jrKUFvNChI2VMnb+R937aSVSrIP40shfXnNUBaxFQpXyPBro6oaIMdiyzwn3jXCjcDeIHHQdb4d5zJER1s7vKRqu+RO+gxLY8ObqvLtGrfJIGuqqZMbA33Qr2zLmQk2E9HtPLCvZeo6D92eDnHadatA2jWgINdNUwB3c4w/1L2PE9mEoIi4eeV1jh3uVC65JID6dtGOXLNNBV4x05YPXbN34Jm7+GsmIIDIPul0LPUdBjOIRE2l1lnbQNo3yRBrpqmvIS2LbUCveN86w7J4m/tUBYz1FW7z2ik91V1kjbMMrXaKAr16mqgj2rrLZM5pewf6P1eFyyFey9RkF8irUcgQfRNozyFRroqvnkb7GCfeNc2PkDYKBNR6vv3nMkJJ5v3ZjDQ2gbRnk7DXTlHof3Q9ZX1hUzWxZDxVEIagNJw6yj9+7DINj+8NQ2jPJmGujK/cqOWLfJy5wLWQSGELEAABLpSURBVPPgSD74OawrZY5d7966va0lahtGeSMNdGWvqkrY9ZN1UjXzSziw1Xq8/VlWz73nKIjtbVvfXdswyps0OdBFZATwIuAPvGGMee6U7Z2AN4EI55gpxpi5de1TA72FMgbyNjrDfS7sdv4/EJl44oqZjoPBP8CtZVVvwxQcLWdItyiG94lnWJ842keEuLUWperSpEAXEX8gCxgGZAMrgBuMMeurjZkG/GyMeVVE+gBzjTGJde1XA10B1t2VsuZZ4b7tW6gsg5C2JxYR63aJdVcmNzl0pIzXv9vKV2v3sSXvMAD9OrRmWO94hveNo1d8uLZklK2aGuhDgCeMMZc7v34IwBjzbLUx/wK2GmOed47/qzHm3Lr2q4GuTlNaZE1i2jgXsuZDySEICIauQ62ee88rICzWbeVsyStm4focFqzbx8+7DmEMJESGHD9yPycxkgB/71gWQfmOpgb6OGCEMeZO59c3A78wxvy22ph2wAIgEmgFXGaMWVnXfjXQVZ0qy63lB46tM1OwExDrxh3H1pmJTnJbOblFJXy9IZeF63NYtnk/ZRVVRIQ6uKRXLMP7xHNhj2hCA93bJlItkzsC/Q/Off3VeYT+b6CfMabqlH1NACYAdOrUaeCOHTua8GOpFsMYyFlrBXvmF7BvjfV4VJLziplRkHCO2xYRO1xawdKsPBasz2FxZi4FR8sJCvDjgqRohvWJ49LecUSHef6aN8o7uaPlsg4r9Hc5v94KDDbG5Na2Xz1CV2fs0C5rCYKNX8L2ZVBVAa1ioecIK9y7XgQO95zILK+sYsW2AyxYn8PC9TnsPnQUERjYKZLhfeMY1ieeLtHuOwegfF9TAz0A66TopcBurJOiNxpj1lUbMw/40BgzQ0R6A18DHUwdO9dAVy5x9JDz5h1fWouJlRWBI9R5844rocfl1i353MAYw/q9hc6+ew7rnbfGS4oNY1ifOIb3jSelQxv8/PSkqjpzrrhscSTwD6xLEqcbY54RkaeANGPMHOeVLa8DYYABJhtjFtS1Tw105XIVpbD9O+fNO+ZB0R5rEbFOQ05MZmrbxW3l7DpwhEUbrHD/afsBKqsMseFBDOsTx7A+cQzpFkVQgL/b6lG+QScWqZbHGNjz84n13XOdV9nG9nFOZhppTWxy0yWIh46UsTjTOqn6bVYeR8oqCQsK4KKeMQzvE8fQnrG0CdGlB1T9NNCVOrDtxBUzO78HUwXh7Z037xgJiRdCQKBbSikpr+T7LftZ6Oy77y8uI8BPGNItimF94rist05mUrXTQFequsP5sGm+deS+ZTGUH4HAcOciYqOg+2UQEuGWUiqrDKt3HbROqq7LYet+azJTcoc2DO8Tx7C+cfSM08lM6gQNdKVqU34Utn574uYdh/PALwBiekO7FGtt93b9Ib4fBIU3ezmbc4tZsH4fC9fn8PPOQwB0ahtqnVTtE8fAzjqZqaXTQFeqIaqqrLVlsubD3tXWDbQP5zk3CrTtaoV79aBvFd1s5eQWlrBoQy4L1+/jv5vzKausIjLUwaW9rZOqFybFEBKoJ1VbGg10pc6EMVC0z5rItDfd+ti3Bg7tPDGmdQdnuB87kk+BNgkuP9lafGwy07p9LM7MpbCkgmCHH+d3j2F43zgu7RVLlE5mahE00JVypSMHYF+GM+idYZ+/yTrRCtbiYtWP4tv1h7bdXDaTtbyyip+2HTi+zsyeghL8BFI7tz1+SWSiTmbyWRroSjW3siOQs85q1RwL+tz11uqRAI5WVh/+2FF8uxSrT9/EK2uMMazbU8gCZ7hn7isCoEdc2PFFxJJ1MpNP0UBXyg6V5ZCXaYX7sbbNvgwoK7a2+zmsG3u0S4H4/idOvjZhueBdB45YR+7r97Fi+0EqqwzxrYO5rI+1iNjgrlEEBuhJVW+mga6Up6iqgoPbnCddqwX9kXznAIGo7qeffD2D5QsOHj55MtPR8krCgwIY2iuWYX3iGNozRu+j6oU00JXyZMZA4Z5qJ1+dQV+w68SYNh2r9eSdQd+6fYNPvpaUV/LfzftZsC6HRRtyyD9chsNfGNw1iuF94xnWO474NsHN9AMqV9JAV8obHTlw4sqa4ydfN2MtlwSERlc7ik+BdgMgsku9J18rqww/7zzobM3ksM05mal/Qpvji4glxYbpZCYPpYGulK8oLXaefE2Hfc6j+dwNUFVubQ8Mr+Hkay/wr7m1YoxhS14x89dZyxCs3mVNZuocFWrNVO0Tz8DOkfjrSVWPoYGulC+rKIO8DSeO4vetgX1rodw68sY/0FqU7Pi18v0hri8Ehp62q5zCkuMrRC7fYk1matsqkEt7xTK8bzznd4/WyUw200BXqqWpqoT8LdWurnH+e/SgtV38rDs+nXTyNQVCIo/voqiknG+z8ljovDNTkXMy04VJMcfvzNS2lXsWNFMnaKArpayTrwXZp598Ldx9YkxEJ2e4DzgR9OHxlFUaftp24Pg6M3uPTWZKbMvwPnGc1SmC7rHhugSwG2igK6Vqd3j/6SdfD2w5sb1V7EmXUJr4FNYeacvCDdZJ1WOTmQDiWgeRFBtO99gwkuLCSIoNJyk2jEg9kncZDXSlVOOUFll9+OpBn7fBun8rQFBriE+Gdv050LoXO0rD2VbsT9YhPzYcNKzZDwfL/ADrZGp0WKAV8rHhJMWF0T02jB5x4US1CtSraRpJA10p1XQVpdZyBqeefK04WuNw4+eg3BHOUb9WFJlQDlQGk1sWxMHKYIoIpYhQKhxhhIa3pXVkNFFR0cTHxpHQLo6ottFIcBtw6LXxp6or0APcXYxSyksFBFm37Wt/1onHqirhwFarbVNaCCWFUFoAJYVIaSGBJQUElhTSprSQhJJCTGkhVUd3Y0oKCCgvti6pL3R+7Dj9KSvEQXlAGCaoNQGtInCERiDBrSGoDQS3tv5SOO3fNtbHsccCWs4qlBroSqkz5+cP0UnWRwMI1p3mAeuXQWkRlBZiSgooOHiAvbk55O3P49DB/RwuOEBp8UEcR4sJLzlCeMERIvz20jZgO23kCKHmCIGVh+t/Uv+gWkK/Eb8Y3HR7wqbSQFdK2cPP37rVX0gEAkTEQ0Rv6H3KsPziUjbnFrMpt5hvc4vZlFvEppxicotL8aOKMI4Q4yilT1tDzwjoEl5Jp1YVtA8qI9L/KH6lhdX+enD+W5xj/VtScOJ6/boEBNcS+q0hOKL2bcd+Mbjpl4IGulLKo0WFBREVFsQvukad9HjBkXIr3HOL2ZRjBf27+4rZu7Hk+JjAAD+6xYSRFOv8iAuje2w4naNCcRy7lV9lhRX0p4Z+qTPwq7WRTtpWuPfEmPIj9f8gASEnQj71NhhyjytfJuspXL5HpZRygzahDlIT25KaePJKlEUl5ceP6DfnFrMpp4hVOw8yJ33P8TEOf6FrdBjd446FfThJcVEkxnQ6s+WFK8ut9lFJwem/GI4/Vm1baPPculADXSnlU8KDHZzVKZKzOkWe9Pjh0gq25B07mi9mc24Ra3cXMDdjL8cu9vP3ExKjQk+6vDIpNpyuMa0IdtSx5IG/w1ri+AyWOXalBgW6iIwAXsQ6n/GGMea5GsZcDzyBdd463RhzowvrVEqpJmkVFEBKQgQpCREnPV5SXsmWvGNH81brJiuniAXr91HlDHo/gc5RrZwBf2LSVLeYMI9a26beQBcRf+CfwDAgG1ghInOMMeurjUkCHgLOM8YcFJHY5ipYKaVcKdjhT9/2bejbvs1Jj5dWVLJt/+GTjug35RSzJDOXCmfSi0BCZMjxGbFJcda/3WLDCAtyfwOkIc84CNhsjNkKICIfAKOB9dXG3AX80xhzEMAYk+vqQpVSyp2CAvzpFd+aXvGtT3q8vLKKHfmHyco5cUS/ObeYZZv2U1ZZdXxch4iQk47ouzuXRGjO9W4aEugdgGq3TiEb+MUpY3oAiMh/sdoyTxhjvjp1RyIyAZgA0KlTpzOpVymlbOXw93OGczgkn3i8orKKnQeOnHQydlNuMT9szae04kTQx7UO4s7zu3LXhV1dXpur/iYIAJKAoUACsFREko0xh6oPMsZMA6aBNfXfRc+tlFK2C/D3o2tMGF1jwri874nHK6sM2QePHG/dbMotIrZ188xebUig7wY6Vvs6wflYddnAj8aYcmCbiGRhBfwKl1SplFJeyt9P6BzVis5RrbisT1yzPldDLrhcASSJSBcRCQR+Ccw5ZcynWEfniEg0VgtmqwvrVEopVY96A90YUwH8FpgPbAA+MsasE5GnRORq57D5QL6IrAeWAJOMMfnNVbRSSqnT6fK5SinlRepaPvcM5rgqpZTyRBroSinlIzTQlVLKR2igK6WUj9BAV0opH2HbVS4ikkeNdxFskGhgvwvLcRVPrQs8tzatq3G0rsbxxbo6G2NiatpgW6A3hYik1XbZjp08tS7w3Nq0rsbRuhqnpdWlLRellPIRGuhKKeUjvDXQp9ldQC08tS7w3Nq0rsbRuhqnRdXllT10pZRSp/PWI3SllFKn0EBXSikf4dGBLiIjRGSjiGwWkSk1bA8SkQ+d238UkUQPqetWEckTkdXOjzvdVNd0EckVkbW1bBcReclZ9xoROdtD6hoqIgXVXq/H3FBTRxFZIiLrRWSdiNxXwxi3v14NrMvtr5fzeYNF5CcRSXfW9mQNY9z+nmxgXXa9J/1F5GcR+aKGba5/rYwxHvmBdW/SLUBXIBBIB/qcMuY3wGvOz38JfOghdd0KvGzDa3YhcDawtpbtI4F5gACDse4y5Ql1DQW+cPNr1Q442/l5OJBVw39Ht79eDazL7a+X83kFCHN+7gB+BAafMsaO92RD6rLrPfkH4L2a/ns1x2vlyUfog4DNxpitxpgy4ANg9CljRgNvOj+fCVwqIuIBddnCGLMUOFDHkNHAW8byAxAhIu08oC63M8bsNcascn5ehHXzlg6nDHP769XAumzhfB2KnV86nB+nXlXh9vdkA+tyOxFJAEYBb9QyxOWvlScHegdgV7Wvszn9f+zjY4x1Z6UCIMoD6gK41vln+kwR6VjDdjs0tHY7DHH+yTxPRPrWP9x1nH/qnoV1ZFedra9XHXWBTa+Xs4WwGsgFFhpjan3N3PiebEhd4P735D+AyUBVLdtd/lp5cqB7s8+BRGNMCrCQE7+FVc1WYa1P0R/4P6x71LqFiIQBnwC/N8YUuut561NPXba9XsaYSmPMAKybxQ8SkX7ueu66NKAut74nReRKINcYs7I5n+dUnhzou4Hqv0UTnI/VOEZEAoA2QHPfy7Teuowx+caYUueXbwADm7mmhmrIa+p2xpjCY38yG2PmAg6xbjberETEgRWa7xpjZtUwxJbXq7667Hq9TqnhENb9g0ecssmO92S9ddnwnjwPuFpEtmO1ZS8RkXdOGePy18qTA30FkCQiXUQkEOukwZxTxswBbnF+Pg5YbJxnGOys65Q+69VYfVBPMAf4tfPqjcFAgTFmr91FiUj8sd6hiAzC+v+yWUPA+Xz/BjYYY/5WyzC3v14NqcuO18v5XDEiEuH8PAQYBmSeMszt78mG1OXu96Qx5iFjTIIxJhErIxYbY246ZZjLX6uApnxzczLGVIjIb4H5WFeWTDfGrBORp4A0Y8wcrP/x3xaRzVgn3X7pIXXdKyJXAxXOum5t7roAROR9rCsgokUkG3gc6wQRxpjXgLlYV25sBo4At3lIXeOAiSJSARwFfumGX8znATcDGc7eK8CfgE7V6rLj9WpIXXa8XmBdgfOmiPhj/RL5yBjzhd3vyQbWZct78lTN/Vrp1H+llPIRntxyUUop1Qga6Eop5SM00JVSykdooCullI/QQFdKKR+hga58johUVltVb7XUsCJmE/adKLWsGqmU3Tz2OnSlmuCocxq4Ui2KHqGrFkNEtovICyKS4Vw/u7vz8UQRWexcuOlrEenkfDxORGY7F8FKF5FznbvyF5HXxVp7e4FzdiIicq9Y65ivEZEPbPoxVQumga58UcgpLZfx1bYVGGOSgZexVsMDa4GrN50LN70LvOR8/CXgW+ciWGcD65yPJwH/NMb0BQ4B1zofnwKc5dzP3c31wylVG50pqnyOiBQbY8JqeHw7cIkxZqtzAax9xpgoEdkPtDPGlDsf32uMiRaRPCCh2qJOx5a0XWiMSXJ+/SDgMMY8LSJfAcVYqx9+Wm2NbqXcQo/QVUtjavm8MUqrfV7JiXNRo4B/Yh3Nr3CuoKeU22igq5ZmfLV/lzs//54TCyP9CvjO+fnXwEQ4fgOFNrXtVET8gI7GmCXAg1hLoZ72V4JSzUmPIJQvCqm2UiHAV8aYY5cuRorIGqyj7Bucj/0O+I+ITALyOLGq4n3ANBG5A+tIfCJQ2/K5/sA7ztAX4CXn2txKuY320FWL4eyhpxpj9ttdi1LNQVsuSinlI/QIXSmlfIQeoSullI/QQFdKKR+hga6UUj5CA10ppXyEBrpSSvmI/wdIvGMHEowD4AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6p6v9OSfcPD"
      },
      "source": [
        "## Getting a feature vector from a trained model\n",
        "\n",
        "Let's demonstrate the Global Average Pooling 2D layer...\n",
        "\n",
        "We have a tensor after our model goes through `base_model` of shape (None, 7, 7, 1280).\n",
        "\n",
        "But then when it passes through GlobalAveragePooling2D, it turns into (None, 1280).\n",
        "\n",
        "Let's use a similar shaped tensor of (1, 4, 4, 3) and then pass it to GlobalAveragePooling2D."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-b_0KHOkP5O",
        "outputId": "369cdb95-8d03-40d5-c02f-dec44649338a"
      },
      "source": [
        "# Define the input shape\n",
        "input_shape = (1, 4, 4, 3)\n",
        "\n",
        "# Create a random tensor\n",
        "tf.random.set_seed(42)\n",
        "input_tensor = tf.random.normal(input_shape)\n",
        "print(f\"Random input tensor:\\n {input_tensor}\\n\")\n",
        "\n",
        "# Pass the random tensor through a global average pooling 2D layer\n",
        "global_average_pooled_tensor = tf.keras.layers.GlobalAveragePooling2D()(input_tensor)\n",
        "print(f\"2D global average pooled random tensor:\\n {global_average_pooled_tensor}\\n\")\n",
        "\n",
        "# Check the shape of the different tensors\n",
        "print(f\"Shape of input tensor: {input_tensor.shape}\")\n",
        "print(f\"Shape of Global Average Pooled 2D tensor: {global_average_pooled_tensor.shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random input tensor:\n",
            " [[[[ 0.3274685  -0.8426258   0.3194337 ]\n",
            "   [-1.4075519  -2.3880599  -1.0392479 ]\n",
            "   [-0.5573232   0.539707    1.6994323 ]\n",
            "   [ 0.28893656 -1.5066116  -0.2645474 ]]\n",
            "\n",
            "  [[-0.59722406 -1.9171132  -0.62044144]\n",
            "   [ 0.8504023  -0.40604794 -3.0258412 ]\n",
            "   [ 0.9058464   0.29855987 -0.22561555]\n",
            "   [-0.7616443  -1.891714   -0.9384712 ]]\n",
            "\n",
            "  [[ 0.77852213 -0.47338897  0.97772694]\n",
            "   [ 0.24694404  0.20573747 -0.5256233 ]\n",
            "   [ 0.32410017  0.02545409 -0.10638497]\n",
            "   [-0.6369475   1.1603122   0.2507359 ]]\n",
            "\n",
            "  [[-0.41728497  0.40125778 -1.4145442 ]\n",
            "   [-0.5931857  -1.6617213   0.33567193]\n",
            "   [ 0.10815629  0.2347968  -0.56668764]\n",
            "   [-0.35819843  0.88698614  0.52744764]]]]\n",
            "\n",
            "2D global average pooled random tensor:\n",
            " [[-0.09368646 -0.45840448 -0.2885598 ]]\n",
            "\n",
            "Shape of input tensor: (1, 4, 4, 3)\n",
            "Shape of Global Average Pooled 2D tensor: (1, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RR3FiwnOlaQp",
        "outputId": "92549e09-563d-46c5-b2a0-5be8e66bf21e"
      },
      "source": [
        "# Let's replicate the GlobalAveragePool2D layer\n",
        "tf.reduce_mean(input_tensor, axis=[1, 2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[-0.09368646, -0.45840448, -0.2885598 ]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJWcn68nnFN7"
      },
      "source": [
        "> 🛠 **Practice:** Try to do the same with the above two cells but this time use `GlobalMaxPool2D`... and see what happens.\n",
        "\n",
        "> 🔑 **Note:** One of the reasons feature extraction transfer learning is named how it is is because what often happens is pretrained model outputs a **feature vector** (a long tensor of numbers which represents the learned representation of the model on a particular sample, in our case, this is the output of the `tf.keras.layers.GlobalAveragePooling2D()` layer) which can then be used to extract patterns out of for our own specifc problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kG7hqnqnfLC"
      },
      "source": [
        "## Running a series of transfer learning experiments\n",
        "\n",
        "We've seen the incredible results transfer learning can get with only 10% of the training data, but how does it go with 1% of the training data... how about we set up a bunch of experiments to find out:\n",
        "\n",
        "1. `model_1` - use feature extraction transfer learning with 1% of the training data with data augmentation \n",
        "2. `model_2` - use feature extraction transfer learning with 10% of the training with data augmentaton \n",
        "3. `model_3` - use fine-tuning transfer learning on 10% of the training data with data augmentation\n",
        "4. `model_4` - use fine-tuning transfer learning on 100% of the training data with data augmentation \n",
        "\n",
        "> 🔑 **Note:** throughout all experiments the same test dataset will be used to evaluate our model... this ensures consistency across evaluation metrics."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRC_IsbIs06J"
      },
      "source": [
        "### Getting and preprocessing data for model_1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APQfl6n_rH_N",
        "outputId": "72619943-6681-47c0-c9cc-c350e3f62fd7"
      },
      "source": [
        "# Download and unzip data - preprocessed from Food101\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_1_percent.zip \n",
        "unzip_data(\"10_food_classes_1_percent.zip\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-18 04:01:10--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_1_percent.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.9.208, 172.217.2.112, 172.253.115.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.9.208|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 133612354 (127M) [application/zip]\n",
            "Saving to: ‘10_food_classes_1_percent.zip’\n",
            "\n",
            "10_food_classes_1_p 100%[===================>] 127.42M   171MB/s    in 0.7s    \n",
            "\n",
            "2021-02-18 04:01:11 (171 MB/s) - ‘10_food_classes_1_percent.zip’ saved [133612354/133612354]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAnG5no1sTvi"
      },
      "source": [
        "# Create training and test dirs\n",
        "train_dir_1_percent = \"10_food_classes_1_percent/train\"\n",
        "test_dir = \"10_food_classes_1_percent/test\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igbHaCDPseDo",
        "outputId": "0626ff5b-4f68-4e3b-b83a-c6b3cb6f5839"
      },
      "source": [
        "# How many images are we working with?\n",
        "walk_through_dir(\"10_food_classes_1_percent\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_1_percent'.\n",
            "There are 10 directories and 0 images in '10_food_classes_1_percent/train'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/chicken_curry'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/hamburger'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/sushi'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/grilled_salmon'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/ice_cream'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/fried_rice'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/ramen'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/pizza'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/steak'.\n",
            "There are 0 directories and 7 images in '10_food_classes_1_percent/train/chicken_wings'.\n",
            "There are 10 directories and 0 images in '10_food_classes_1_percent/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/steak'.\n",
            "There are 0 directories and 250 images in '10_food_classes_1_percent/test/chicken_wings'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jZl0D9IsmUs",
        "outputId": "c129af6b-ae7d-45bb-a6ba-29c66feaaa61"
      },
      "source": [
        "# Setup data loaders \n",
        "IMG_SIZE = (224, 224)\n",
        "train_data_1_percent = tf.keras.preprocessing.image_dataset_from_directory(train_dir_1_percent,\n",
        "                                                                           label_mode=\"categorical\",\n",
        "                                                                           image_size=IMG_SIZE,\n",
        "                                                                           batch_size=BATCH_SIZE) # default = 32\n",
        "\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                image_size=IMG_SIZE,\n",
        "                                                                batch_size=BATCH_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 70 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGyi_vbatHVM"
      },
      "source": [
        "## Adding data augmentation right into the model\n",
        "\n",
        "To add data augmentation right into our models, we can use the layers inside:\n",
        "\n",
        "* `tf.keras.layers.experimental.preprocessing()`\n",
        "\n",
        "We can see the benefits of doing this within the TensorFlow Data augmentation documentation: https://www.tensorflow.org/tutorials/images/data_augmentation#use_keras_preprocessing_layers \n",
        "\n",
        "Off the top our of heads, after reading the docs, the benefits of using data augmentation inside the model are:\n",
        "* Preprocessing of imges (augmenting them) happens on the GPU (much faster) rather than the CPU.\n",
        "* Iamge data augmentation only happens during training, so we can still export our whole model and use it elsewhere."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7n1gYXGvTbT"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "\n",
        "# Create data augmentation stage with horizontal flipping, rotations, zooms, etc\n",
        "data_augmentation = keras.Sequential([\n",
        "  preprocessing.RandomFlip(\"horizontal\"),\n",
        "  preprocessing.RandomRotation(0.2),\n",
        "  preprocessing.RandomZoom(0.2),\n",
        "  preprocessing.RandomHeight(0.2),\n",
        "  preprocessing.RandomWidth(0.2),\n",
        "  # preprocessing.Rescale(1./255) # Keep for models like ResNet50V2 but EfficientNet's having resclaing built-in\n",
        "], name=\"data_augmentation\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Uf2sDFgwXKQ"
      },
      "source": [
        "### Visualize our data augmentation layer (and see what happens to our data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "Nn-_iBYTyjzO",
        "outputId": "40f84af6-cc79-4dff-f1f0-aeb8d4fe7b3e"
      },
      "source": [
        "# View a random image and compare it to its augmented version\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import os\n",
        "import random\n",
        "target_class = random.choice(train_data_1_percent.class_names)\n",
        "target_dir = \"10_food_classes_1_percent/train/\" + target_class\n",
        "random_image = random.choice(os.listdir(target_dir))\n",
        "random_image_path = target_dir + \"/\" + random_image\n",
        "\n",
        "# Read and plot in the random image \n",
        "img = mpimg.imread(random_image_path)\n",
        "plt.imshow(img)\n",
        "plt.title(f\"Original random image from class: {target_class}\")\n",
        "plt.axis(False);\n",
        "\n",
        "# Now let's plot our augmented random image\n",
        "augmented_img = data_augmentation(tf.expand_dims(img, axis=0))\n",
        "plt.figure()\n",
        "plt.imshow(tf.squeeze(augmented_img)/255.)\n",
        "plt.title(f\"Augmented random image from class: {target_class}\")\n",
        "plt.axis(False);\n",
        "# print(augmented_img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tI48/fpl3vP0JHn9kF2+EFGEYhBQVYwTnG5xvML7IjQIxRlLKSBZiiLSrNf3yiKODZzm8d42+vwvaI5qKDmEwxmGNxVqHiCMJRBRBaIxHVFFi0VkxRU7FItqhBFJOhKSkJKPWCClFVE5sQgYRvuW7fvRFa25f1rherDznvWANBAPGWYxzxeigZDLZgBhFbMY5g/cVaYCuHUjdQIqRZBKmAl8ZvK8RhSQRM7FUswrjK1atkp1DiMTYk9OAMR4jPXHosUnZruGR3SldiBx1AcXhXI11FWDJ2dLlyFBF8qU5k0/bYXZ+Gz/Z4MThxJJQyEpOgZAiQ1I2oSdphROL5iKUjEZSEBJC7DL7hz3/8iPP8itXb7Jcj4Z1XEoRg4iAgqgAikNpHEw8GHNihAF0vKZ8htGAntRFi2CkvJqMZEQNRhzZKNZNqKsFqVdu3niO6x+7zo2P3+a5a7e5deMeKViWhwOd2WedE5KgyQbvMuLL7TTDpHJsNROmtvxqZC9KsIKKg6QwGBALxhIQQlK2ZnPm2551POLO8RGrMKAC2QvqI2o8Hk+OkWEwGCuICJLBZDBWsMYAmZADGXAm0u9vWD5zj3Y/IMOEyjbYxmN9wjqPOIexStZEipEYFKuRyoOxjiiQs5CTgRjROJBTIubM7aHlzmagzzVGLbaRMgFAVdVMp1OmVUVVNyyXS24+e8i920oYDOcvlE3TSMIoWGvBO+ykoplXqPT0XYcAW7MtrN5maizbsxrJljYKh+2GLvR4a2kmDa72JISkCYvBiWAFvHP4yhYZoiemNV0LMTb4uoFcoTkz9Jl2o5AzdV1hncU1lhSFxUIIfYfmDDgCoCazyoFdqanU0xiltxB1Qh8qkmawEcGRASOCNcpOlXnzXjFS1/cHUq5AK4SMFaXvO1IMVL7Gm9F4ZdCsKDWqjqSZVdtxvD7iyvZFLl+8zNXr93j2uQMWWw2LxYy9RYWzUHlhyEqKiZh7UMGI4I3BWIu1pjg6omVT8R7fTJgudglhQ0oDQ38M9BgBKxZUEDUIFlUlkUEUKwYhYSxoFrJmVKQYVlGESM6pmGGxZdNQCzkhAmJMUdUM+RNeAn0VxvV842gaYfCQBYwxIEJSyCJgLWIUiOQY6VNLt0rkDoYuQk5YB662THZnTCYT4hBZHh4wpDVmmpnPdhhyQzQW8RnSkhw3qImEQUkxk9VijWVSCRcWFUcrz6YbSAqVb6jrhsp7DNCxwV8ybL15it8zuCl4W+Ftg4jDEDEGQuwZQk8Xerqg5CyIsSgZawxKRnNCFWKC5Sry5FN3eP+Hr3L3aEXKOioCJ+8dI1I2HdViPJ11zGZbTGYTvAdnikdsTLlGhNFr1dFrVlTleSN78p05Mb1SMXSG/Rt3eeapD3Hr+gHLg5bVccutO4fc2T8mqaDG06Gs0wA547MgRlELYDECtRWmzjK1FU5qjKkQa8neEI0lekc2iWACWVseefwily/uMN2F46NDYrPC75b5YZ7Jk0R2AYwh+MyQweno/5uE2IyoYiSSk4LJeITUt2yuHxPvgh8miFRIBbiBrJEYK4QBSZEceuImkbuI5B5tBIIjCKCRlBwxRjQlVJVlUA5b6AdLFGEiULtMIoB1WDF4a/G+YtP23HzuHndvtSyPHDlnYoyIzZCh7yMxGrJmrLVMaoM1mbZbEsPA1nyHxjd0eYXzlpm37EnN3qxif7XhXtexyYHGVky3ZkyrGs0DKQTIpqy1Fq8qxzV9uyb3CxpqYrLUzYQ+bOg3HSsxpIlDZ5BCSz3xNE2FLipSEA4PW6JYBIfJymY90NspglCbHm8FYwxRM9kKYn2RixJaYGxCjLLtDU+4OTm33DxWQsgkk7BiOXduDzEVYQg4Z0g5oZhiYNVjxDPoQIgtq24FusuFnV2G1vDs3Zt8/OM32d1qaD79MttTj7NCtErXtogEjDWQhJwtxhT9x2YiCbHgakPOjjrPmPZ7dG0ghERKuTgvKhikxIc5Px9dGguaAoGAd+W5UVOMrElYEqp5fHXNYIxHxKJJUUlF50cVFWGMQh/CuC6mStsoxil1Nigw5EzCkBEcgmhGkxZPIQ6kIUJw5AzGKIJiMgxtT+gyQ9vTthuMz4hUbHpluVyheYoRJQ4RSRHNmYSSLLjK461FojLBcX53h6P2mOMuY3LGWoPzNSEPDL5jfr7BbXuMK4LiXML5iMiAaELwhCjEnEgaiEHIMRfjIwKSIacy4aIMg3Dj9ob3f/gaV2/eY4gvvCCTc/4E42qMef7YGcP2Ysp8VuGtFAERnjfKIkUQilEdP6stx897sYznGA72e578wFPceOaI4/0B0QpNlqG39IMhZIeKQcUStUNsRI0QUxH6GCnCpxlLwkuisq7QB2Kw1tDMaybbcyJlXVwtPPaW83zu570N5wY27V3iVFg7wfYVViqsDWTJDGQiPftDYj4YZs5i8uiZOyXbDFJCUGMd0mU46DD7YIcK1ICNYBMqCtmiAuRAToHUJ3InaA+aYMiCDjCIIhpIKRPVkVVIOdJng2FKFsgxsdNYFsZyGAa6WIymwaAqLJc9q2VEsdSNIaWAdRkRxbuKvh/o+0SIkapxeOcRMl27YbNeM5/tUTVzWr1LPwQQy6LxzOYzpG4Yjlbc2RzTHbeEnNjedWzvzDEi9N1AGFrEKNZaYrSkfsq82ubilXM888whvXbUNtF1A+tVQtMciZmmUkLbsXt+wnRqGPqBzUpJqqBCLZYhw2ECK4p3UCUFAoMmstQYZxA1oA4lkXPEmIzHsagrHr2wTZbIrWUgoqgIs60Ji/mc4+MlB8dL2m6DYlBx5OxJ2RSvWBSDYjUzcXBpd043LDg4usNTH7vK1sxTv+kSlXeIG3BVJqVATD02GqwdUGMRY7GuUASqinMGao/kKWm2y6yPDGFN2gRyGkAUMUJWLZut0TIWEaBsvikbjHjEFIdDcyBpQKTEkvnk926ylI1PirOQVVGl0IfiH864xvOBIyPUSajFkDNFSUUQY7AIKWQ0CykbcgKijFGXoJoIfUdIytB2CBUy9pGycvvmMflOR+0n1C7RtgkZMk4tkBCxYDzgCEnpU6aLGbGeppnShQFnS/ieVBkkkebgdmqsb0AdXav4Bqwt4WhZ6poYbHH9NRLVEskoJcxXTchIfcSUOTzKfPDJG3zoqWdZtYGsL/ya2Gnv9fR3AlTOsLuomTcWY05vdQYRg5bAbbxvLmHMyBeBKfREzhwetdy+eY9nnjrmmY/eI6wNJlucK/dK4knGgbdYZ8ErtSS8VYx4FMswKCFANyTCUIQDa0liSTkhklhMarbeNOfSE5dZLmfcuNVz4coOb37rZc4/YVku17TDPks9Zi0DuUTJxfhFMJpxYmhSxSqVZ3IhYHNATCZUhohhiGVDs0HYWcNW8FhRkgZUAiIZQ42IL2FmjGXzyRajioqSVRk6JVtHr4mUMyeFLwokVWyObAnMETpVtkJm4SyddXSayDGCCikL/ZBJqjRTy/kLC5zdYbGYkFMiJ2Wz2bDeAHhmi/KrgUaUGCPrdsP5848ymU8YRFluAqsuspuUee0KHy9gKseQI0dHLet1x2Y955FHLrO7u0eKG7J2YCJ97xmCZ+IMe7uG45Vw43BF7S2u9gy9IcZAtzGY5PBZsNkzn0+I/UDbGJZtwvrCRw+mYj/0NKJkB84bRDNJLaoNGAr/mKVw/zZhVDGSaCRybuJht0Jjz2EXiNZgnEVMpvKZyjs2XUcIPSqZnDM5GyQH6sozm8ywIpAHZtMJl8/tklLHwdGGp6/eY7Y4x8VzgvXCZFYRh+II5BSISTBZsKZCjMNYg9MSQYgrAljXU2bzbfpuizB0DH0YtZdRbnIxjkbJScY4sHDLWTPGQDZpdLwSYiBrsVMQsaMtU0ljZFqIPc1lfh/KuF7bWbPWhAwO2Via7FBTkYzFWUNSIUeIMRWPTIth1QxiwJnCs6mzxGQwajHGlVA7KLFLZB3IXgm+JyNU3iNmhkoNxEJ2dz1tzqyHzKq3rEJFUkNV1zRNhfMOjCmJg90Zkcxmc0RdVQzZ0VgHFE4oi4HsGAYFBetM4WwtYAKSTfHJBZRA12WuXjvig7/yHLcOl4SUxl3rhUkVKdziaQ/WGmE+8ZzfmTGdVIVSGY2ljucXGiGjFGEQakQKf1WoYaXvDb/8/qv8/M89zb0DJYYaS0Xlhfl0jq8suRKYZCa1ZbqY4upMNONujRkF3pKiY7Ue2N8PrJY9Rhqcm5BzpJlZHn3LJR576xbNwjC7tM3O42+hngpuMhA4JtuOQXpM4/FTiyUjJNarns0Q8OOGe/N4IMTEdlWxcEKdIzkrHY51sqyH4nnuKOwpOJPBJEQjWROSDEIJx3KO5BQQMWVObNmEUnJ0fSJHw6BS1iWDsxkxSieB1maytexWFWsvBIGlyYVfThmJiZSUrBBzJKVAM6nYPT9nZ3sH7wI5ZdquY7Ves1pB3cwRI8QEzlmsM7Rdi7HKYj7HiGW16VhlpU2JWWNBA20K4CzeVvQhsjza0LWHxCA8cuUce+emWJsIcU0XYNNlohwh91p6BWxHxuIrz2QyY30cWB8GUjRs+8Jxbu9skTVyvDri3moJxoGtSKrEEIh2Qu0szmasqXCmBnWIUbwIOUMUIFk0Z4SMl4SYnvOTCntuxrP31hz2iSFH1Fp846hDZBIbrMvEJAwhYURpnGd3d85iPiWLJYkiJrNYNFzQ8zx3+y7XbxyzWNxl0tRsLQRjLc6VMD1p0f9kTeFZRXHOEdWQYqHVrBN8Y2hSzWyxTd+tCGFNzpGYC912kgMRLdmiEtmbEv7noURTptBvUKgDjC38bi5Jr5xjSRyMDpIWe01KD2lcgxVWJnFgeprOYqLixINNRMmE6IlBSBjEJmzOmGyxWoxLNfE0s5pBIa4ipOKxCRYjnkos4MkxE3JHMAObZl48lpjQFIhDZtMV/mzVZVZ9ZtMbrJswncyp6grvBKkM1bkZ7pEFZtoz9CuGITOtG8TNELuD0iC2ArGEEBBRmnrCpNrCmxpDREwZU8qJEIV7B4EPfvQ5nnruFlFL4ko1/yq+9cSonvzrnGF7PuHczpy6dhgrxaKc4lOVkvYemS5QX3Zc6UhaI2ZCjom7z0b2b2VuHm4I0uKso/Gw7DuqSYedg9sJzBYeJBDypvQxCoBYsMbi8dQLSz2fcOeGsjkq4dVkUnP+0SkXP22K3wrgobIV1XyC5p4UA2HIiPE4V+G8YE1H4w3g6btimHJKVFXFOkeG5YoDMWxVjkoNsff0Ueg002UKNWMyj1WRcwLZUmZBHJocKhYxoHlARDHGEkwi2YyqEJ0hhEwYlJCElAtf7mqHNMKmShw5ZS2W7AyOigEY1BKzQ/JASoZhSBg3oDnh3JTFbMHW1oKt7SmGDu8dh0c97aajbQVfTYhp4OioI04n7G5v0Q+BlCKL+ZT5bIYxQh8GVm1iiBYkkslI8lhb4Z0j2EyKymrZc+vWXUQW7O0kYuxo20zIlpiUVbti3UeyCt54FME6xTpLIrNuA6YStroWay07uzvcvXcXY47ZbFbM5zsgEaxDzQQErB1wxuAYqSnNiHisM2Ma1oBENEUExZuE8Rm78IiZY4877g2RQQTnLZOJJ2bBB0PMlioNTCcVW03N1myKcZCkbGrORGzl2N5esOkS9w6PuH71JluLirq+yMRXGCNYCzEFUkqklJAYRwfFj7ynKWG+tTjvqZuGyWzOtNui644J/UDWkog8MYbFuJqSVBUKBaIRzYmsBisOyCQyhZyzoImkCSUXSsGAppJoNtYWvX0Y4yrWEDWxTi1LqUuCRjM2RcCSjKOTQjJbyVRZqbJDFNQodlZh5zW6CWTNpJwRiSQpPCnSoFoTc0ebWo6rgWVTE4YMGrApoENiOQgHS0fbCiGWXXHLO7z3eG+oanALwZ2f4OYTxMHRsKYPPa12LNdr1qspi8ZR1Z6cI33YgM1M6hnzyRwrBjQWikUzKWY268STz9zll56+xr3Vmpxf4FpP/YAuwPNca/FIoXKW8zvb7O1s4X3JQhoxo3HVsSJhrBrQmlJ2oSQGQoI+zKgXFxnyEbE1WJmidaBNPeRICLCJYEPP7sKxs6NEiXRtoAstajNZHSVExRYAACAASURBVAaHzQZrFGsj3gnb2xMqabgjS/p+yWJ3m3OPTMiuZdVlaq1xNuFsEURRz9BZqrrG4fEKlREMjnbTk4LixDGEzCCRqrIEVVLKdO2A9o6hrYhJSVL4NMkwaYRsPM57Ol9kxg2JXgs1oyYU6kQMWTKZjBpDthCtok4gjskGJ2RvyYuaYQYrn2hJdEAwmWwUowZrLDPbUGWlHyIhJLQdEFtxbm+bvd0Fi7mj8hkrFl85+qGnHyIxGLphw2pZKJ7NqsXbCVtzJQw9i/mMre05vraEVELHlAVjHClHyKV0cTabALBcrglDMbKSFJOU0Gf6HurpHCcV69Ua4lHJAQAqkDRiKmGycAxdMcB37g1cWe+xt7PFdFbTTCrWq56uWzOZesxoNFMGSZbGGioH1oQScY78YonaCq+qp6IxbMBbZc/XZFeTDlrutQnVCdPGk3LPcR4wItTeMZ05tqaOec0YGSgpRawY1AiN81za2SYNkeVqxTMfv818PuPKpR0qa7Ba6MGYlBhyoYpM8UStc9SY4o0jWGvwFTTNjNlsm269ZJkCObUoodCUWcYirEILGHuSuBqfPZcKJiUV+8WYvCocYDGqKpBG2SxXgnlI4xo1gSjHovjGk02mD4E6KA6LSqA1kUGFabb4LKiWJIQ0hlAZ0qCs14k+Ssn8E8gSx8ykJallkxKrFDisDEcoyzCUJJoXfO1ADKkybG515DQwcxPqylM7h3eOeuJYXJwhexOCNMTBYDDMJ1vUTWRoD3jqqadZr1ZcvvRmrE1s+gPEDSwWU2Yzj5EwmrxESoYwJJ67veJffuQqz945IMRUSkH0JIlVhPEk66+q484KoNTOcencLttbE5wv3wmCio51mZStVC1WK1QSgw6se2XV1tSzPRbTbe5tbpI3PVMq6knNcRuIOSJqcGKYTg2TXUOfjmlXmTgICYtaP9YZCHiHWIfBomIQIyy2PUZmHB6taBZKHzbsX1/ia8vlSxfRWshG8N5hxTMMicnE4Y3DS8JaR4qW5dEhXbfBUGGAFBPJCsZb1JRKi6QThtyQkqI60IeesOlYZUesF6jzOCkSYTRjFHJOZFe8buWFOVdKpYpY8I3HikNNIEqmtZFWlI0Xll6JJxliEtjCwWEV5wWTDKql7KfvMq5qOL+3YDFtqKtCUbjKYp2QslL5CZ0kQp8IfSmDatcDRwcbLp4LDH3LbL7Dzu6CyayiDx1GK0CIKZbkiTHFwLjM9u6Evm8ZQsBIxbRWNEf6tkjK1u6cym2DOGJOhNCTciamjJgO4y2TrYbZYkpOjpjWHBzc4/y5KXVjaBqPNYGha5l4h68tqgNJhco6qrrGN+Cc4owbw2Swo8+mRlDj0JTJOnp49NRG2JsaYnAMg+G4zZjaMZ14hjSw6QvvOgyB1ADGYbWUMkkaf0DWFHpgPvHsbu/QDYmbt46Zz28wnXgubM+wzuDVFTmIeXTIFLUZMQ7n/SgXpQrGUlE3c6azQLdoGfpA20ayRowmNBtQW4y8RiyhGE2txpryVPhMTRhjCw1VrGzRcX2h5p3n/zIv99+ZvaxxNQhZLBsHKQU6Z7mYDDOtcCpYiXgBJwYfIWVPJ47KK85l+jaQukDogOxImkgCSRwxltrJLg0s48CGTD94NjGz6gPSwNqVHX1r2uDPCXMcwx2lTjMqt8DaGjGGelZz8cqcrhaOOqXPEWuF2WzB9tYEb7ZoqoGjoyPu3PkAYmvuHd7DuMDO7pymjoikkpnWkphbriIfeeo2Tz59i/VmOJUh1Bd2eRlfALivWsA5w85iweXzO8ymFmsL36OSYBRiRi5IsCiGLgb2jyMHS0M93ePS+UepXWK1f0wOZcPKMZcaXYQ+g0qgmlqMi9y73dEeODQaxBuaeYNvhFzyu6iAJjNWE2SEwGLL45otIsJ61bJctThv8bJkNm+om4qqgcqDqyzZlqSZuoixnkmzzWbdslotqZyj8p62WzPkodA+lUMtVDPP1ExKoXoaWB8JR/sWK5SKjSFQ4QgK0TiI+RT1IqVe1piSAR+35SzFYOJDqV3JiWwSG6scWUP0gliLFYMFoioxC2oyKQ2QwJEgDwyxVKfkdkkiFO9tAq72GGNp6jnnzl2m3RwgmiFZvKlo80C7WTMMS1brYxZbuyy2ZuzsbtF1gdhb+n4g5YT3JakpJpFp2dvdw9pdjo4OaJqMrwZCCnR9QnHE1DOplXN75zDiOD4+pB/aQiVJxteWambx1uNtQww1YjLiI84LVeWZz6bEXpEkWMlkExFj8NYx9YnFJDGpHMYUg2qMjoX1FL6bMu+SQql9Ho3SzDouzB3toITQEzSXlzwWFVE3bLpIUKUbPJOmonIVkk3xIE2pJ0WULJHZvGar3+bW3Vtcf/Ym53Y9W5MrzOoJ1gk2CxrL5ppjJFuLWocxJQpJzpTkGYqtaqpmznS2R7fp6PsVMWxQYgnfM6gRyAaNJWlOzmPeI2JOaljz6AyN1QWf+B+2yFjWVeyAfZn/r/DljWtyRAxBEwMZYyMLkzA54VPG5UxlLI2CSZksnmSV3iTioIS1EnodkzSGLEISS59h1UWO2pajIXIclqS5MtMJOXhMSAz9hqiR0MOwlZlsCZMLFZWfwcGU7CvEW4wNVHNlttUTky0ejghV7Wimjrr2LCY7XLm8zWa95ur1G3zoyQ9z9+Aml/YWwJxhEBwlE0k2DH3m2o1jPvDRG9w77sk68tijJ3SCF6oFXqgaEAFvLee25lw8N6dpSt5fRiJcEE5K5cCQVVm3kVt3AzfuZma7F3jz45/OZGbplke06wFja9QckePACZFqnMGZgMeRoyFtHHldI9EyaAchMdutqCZVSRqGruzY4kevpCQP6qpBQ6YfIrUt1M/x3SV3b+5TNQ3TrTm7uxPqqkLUYWyDsT27uzWTuiYMUw4OHEOfqSuH4EipI8aIqwy+cVifcLandh6TBck1cTBsecUSyDFhcYhv0BxJEhGxpJTQXMp41ESiZoZUDIqSiePbNc4IlUBjLBuUlDPJeXAGQ8JkAxFMVjREUo4YFegSMiRsUrJ2HBzfpK08emGbxaU5k4sTrKlpmgWXr0w5PlaswGRS+hUCKS/p4xbLzTGPkJjPJ2xvb3H3zjGbVc8wcvuIIlZKFGN6rI9cvDRnvgXzOuOsYTUEhmAwpiHGzL2Dm8yabba2ZohklishhAHrFO+Fus40PmGtRZhQ1Ym2X5aNH4P3nq1mgXNSasiNoWsjlRHOzx0XZhVTVxfjKgmIYzZayKWACjvWv4oU3pFcKgkm3nBxR4gps78KxOSZuAnTSum6TIiRPmSGmLAGzFgeRxaMeizljUPnYD5vWK0XrNfHPHNtn52tKdUlhzMe56QY1hRIEYwtb16JlJdU3OisKIJRi6sb6umC2WyXtj1gCEdojhgdk1s4NBuSFgOvGkv9uZryphYWGWtfUy5OiRghZx2V1ryQnBYh60Ma15xKp6pCFtjYyN1qoNXEVrQsvOfkFdGUIBMKb5GUYTAM2RBCoRYGMgMluxITHG8ih/3ASpVlVJJ4rF8AgjGKD4rJEVtFqAcGyUgVaS7P8FOww4CYiEwifuGIdU+7qjnedASxLGY7iHMkCkfjbc1s6mgmx4QUiSGQQuBw/y794W0u7O1x8eIlNMPRcuDDH7vJ08/u0w3x+eLu0eUsdXCnSjBODO/JJlZ5y965Gbt7NVVV3koak90lwTRWCoQcOTjqeO65yM07mcnWHo8+8QSLnYp+WKJZybkUudfWYDJl6UVwxuJMZhh6Qu9PYme8qbG5JraJwWt5fbWGLB1W8ljPV2y0GINzNZVENu0x2/Mp586dp+97rj/7LLefOyBcO+ZNj15gZ7INW0KjU3pdUjcOzWtSOGbawDoWT9P5mhwDWZWUEpVaam/xPuNMQLKlrivOX2i4uLCYg0PCwcAQIk1VYRuHaEeIucyxgZgicQj0ClmlGDUdEHKpf7RCHUvmtu4izmRiJYgppX8MRZ5EQWMutb5ZqQaLDp6YSvmQqFJXENmQJjUzO2VICSuGnb05jz4e8CpoGjhc3iFrQE2kDx193zEMA7PplPl8BkYZYlc23vLuDc47qkpwNqI6lATMZM7EJVJuaftMiBZsqV/WHDk8ukfly6vFc2nYbCBrwNmMcwHnA8ZYMpB0YP8g0W4ghEC7Ghg0s3d+zmTisa4mp4g1wmI2ofFNqSU2xUEwqmBkNM6FptCRVzQYcrbk8WUHh7LdOMK2pR8iyy6jrqKxNd4l2q5nuQk0dcKZhDWJTCQMkZgtTdNgbCYBvrLs7G4x7A/cvNvyzI01i50LbE8F6zIul+qOFIvMGuMw1uG8xUnxXHMqL0SYypXk1mLOrN+hG47o2rbQAzlTalxLdZP1FN0cSyPRMRsyUn15rNhRShmWcSUS0udr1eWFevRXa1zFCVYZNTHRuYynxnrDdrC4oZRmrWImDgNJO7CF72tNIkhmSMrQ92yCso6JhKFxvgzS11gDlROGEGmXAb/lSZUnW4txFuc6rGlRlJwrUlXj5j0EJeWKjc0cq+K7xP5qzWZI+MkM59zzXJK1FidCSopVw6Xzl7l0fo8nLu+yU0Hq16UoOStxUD5+fZ9fevIa9443hfMZ39TihA5XRfOYLRzfXCu/EVAM6Wxacen8nO15jTMWMX5c1BOvVxiGwO3DFc9cbzm4V9FMd3jLb3ozFy/PyWmDSMZXDdbWLGYzthcL7NEdrBa6RqOi3jL0gdQJlfN0rhDylXHQR3SVkMZRNxXJSskaj7uBqsBY1THxhr63zGcV81nFYlZDirTHt3j21sDT62N25ne4dGFayqaKjNK3Pf2mJcUO72qczSQc5AqIxYgPQKX4WqjrUgmQG8XODKZS4rEwkKnCwNApzWTCZFLGn4eSWMmSEDF4FTICKRQbgAWKYVGNVGpYJMOqjfS6wU4SLoPphpKEcw7vLEkFMcLOYo5RR4yZGNP4OnNGjTK1lon3rI6WDN2SejHn0TddQofAZrXk7kFCnAEjtN3Aum1p25bpZMp8MaGZepwH4y3eQ9146qammSi+soBQeYe3Bvn/OXuzHsmu7Erz22e4k10z8yEGkjkoU0qVUIV+6P//Iwqtqm6VUlJKSg5BRnj4aGZ3OGM/7OvBRAElQAThIAJgEBE2nLvP2mt9K1+Y1pXLHJkXi/PCajPOCjkbzutEjIGmsbRdJYQVYyzOOtUhyeSSKDmxnhJp8ZRaKCSmaaJZItIO1CVSak9GENtQiiHlgivq7yzb57MieqAa9XEmIhgdjHQm1imxUjgMwptrQ3lcOUeNlqrgkqkxcb4seFNxXuWGnC02ezDgvQY4xEK7swyL5/nlwo8/PfDuzQ39r2/ofMZ7oBhSrJSUKS5rerJYRBzOQvUqgdlScI2lHXr68ZruciKsgZIv5FK+LAaNlW3JBblmjLwOTQmqwdnNObD5So3Z3EFSfvbPwi/XXKvk7UOslozGdvTscJ2lRstyNkzBMWfIMZFrAFOIUjiZTLYeaktcM8sSKa86hvE03mkM0wjee2KJxFOgxBnp1C4lUrDG4vIISV+EaY48xyf2xhHaI9M5cPfB8I2Drrvh9t0Nbben8YP6ait0zmKAse/5u7/5A7tmIK73vDk6OleBnW4OY+bhceZ//PHPfPvTPUtceR3/qa+pN/3HGs2BixHM9iNicMZyHHq+ut2z69TXi5gtGpBIFJa18unuhT99f+b+3jPuB/7Lf/sVv/3dESFQaqFpO57PE/Oi8cj94cjOd8xJfZ6xVHIS0iJcnhe6XUN7aEnBYpqG1oCsliHt2dWO1TasckIoXyZwtiWFscL1zYG+a7E2YRBuDh1fv3vDp4/3TNPKT5+/52VqGQ9vKVWYl8jlvKiViYxzEWMyiQ6bO6Qsm6lFY8QlJUzf4buWEJ6JKXCZhFAyWXRRQ8xYX2m7HjGOWish1y+MAmsd1EIuBlVatw24qDdRqmEk81UxDAsYGv2Ah0RjYPCOvmmoVv2MndfEEhhSrsRUWWJmcUI/quVsXc5Mz48Ys6PfX1FbR6mWpoPIayJP036n5cz7/chu33N7e81yieQU8E2l73qatsE1Be8bqJDSSushpoXLMnE6BS6Xln7IVCLO6RIy56KeXgp972janconyZFoEWsodSHnQmtHiilgKs1gOV4f+eqba0JIPNwvGBFKNeSSGIYR7zJSwvbA/flWJcjm9YScgFow4jBOoCRqUWZE4yI3V4lUYb43FBqKGDIGxDAtC0JAJOO8w7sBkQWkkEqzQY50wbUbe9Y18PI08f13n7jeD7ibFmfBOaGmQimZlFYk6ELcOY+1LaUaTAZrMrVxtHmgH67oh5V5SqwxAZun9XVAKWVzAAniNCGpGw1LqZFC3pJhbOEf/W9y1elClb5fmNAqJWGdThyN6bhq3mFiz9PLE6vLpJJ4fkqUIEhZKXWmiJCMY/WOqvsPbDHkyhdAhbF6T6qC5rudh2TIsZBDwi0V5zrqmigms2RHjegyYgfVVWR0uKseGyyLTEwp0DmDbz1926honUFcodmsUGKFRjxX+x5pGwYbEYzmpKp6Bv/4bz/xv/70I6d5y1ga83OemC0gYO3PbACjTzwFXli6puH9zZH3Nweaxm+/L+kbY4QQKz89nfmX7565vzcMuwN/83df81e/v8JKUquZsVyWwL9//yOnc+AyBdY5cmgHQq5ctrRarQmSYTkFSo1Iu8MPSlmqSbDF0PqWwfa03rLansSJwAkRQQOrglSH9/ohEjJSC41defuu4fat8P2PF56eCw9PK74XYrVMlwuX84VUim52xZFTxhuhug7rDc0u0neOsdN47TTN1OnCupwpa8YGwzJ/yadRaiGlFZsF3wzsTE9dZsiZWrfku0AUTbCV6sibH9bgdPKsmSs8h+ogdFAKuSRsTXQx0zIjjQFnsalia8R5qE0meWi9pWk8bteAgxRWzHnhMn9Avin4XYuYla5Tl7J1hsYaKIXz5YV3794x7gZubkamy4kQDK03WOsVXGNf9ctIShdytqxJp9bLnKlVPZ66ZDK6TDEeMZWUA2vIHI87pEJJAtVpiqxWShBuv3rLQ76nbR03f33L1WHEusTj/QtXo2FsPL0r7LvI2GdMDWqeF7Mtbeu2eEM11mowYlRbrPJlt1CkqqxEZucCb/Y9j+fKD6eZJWra0TjDskbuP7+wzheurw5cXRdg4WwajFXqlWsMvnV4P7AbDjw9PPDhwyPXV1cMu68Ye8FYlVdqzuQcMNlhS1J/Kk6lApPAW0RaShZ1D4yRZTqzzg8aad4mzU1aVr8slVIyImUbmApVkh66f6FH6yJO7ZQimvii/FLNtVSud3vevntPzA1tPTK/RM7NI1EqcYXT54CtXuP4GHI1GNOTi26u1bsUdWvoPcYI3jqNbWY9dFIOGoksQjU9MYEphRDKtuVdqJt/skEYr/e8/8013a6jSYb5vBJz4jydMaahsa0+WU2lbw3OgRi1Slkx7Fo1XZlSt+tTIaTKh7uZv//Hj/x4dya92i5K3eK85osEwHYY/6yz/uwW6FvP+9sjt1eanhKjm8haDSm3PD4Hvv8h8Phk6Porfv+H3/CHv/01XQMpZLxvmZaFf/7uW+4+nwmrkGOlronBNvSyEGpSv2d1GDy+9thYiaVQmLBtT0ND6xyt9XhxtNLSuQMXKqlGNYbVVRdMrxPLprUJmWozXZe4ORQefxI4DSxPgvlKvVHPzy+kNSBWEDcQFigp0Q6wvxo5XnmGoWIp5JxYQ2RZIzkmiOCKRWKlZmVMWGMxJm5+QmUt9EOP7+B0CqxLgRIRW/HGYsQQi/6JC0KqmVKzCmWiX7g0J8KyICVjvCP7QukiNhvEO8CQqRSXkTaDc3TiqbbFDQNVBLcW3lTPeUmE+0dy7igSKMw0XmiblrQG8pqYTxM5Fg6Hkf2h5ebdwDxZpZptn5lSM0ik7QrO6fJtWYTL2ZCTY7frub7eM/Sd4vaKIcRMyisxXagEcqrshpEkyvWoNWGlo217du2Oi3nh9ubI9fUB6yDGzH6w/P4wsBPPr3/1FX/9myNXXpdTrwfGK3lKat0m1W1W2yY9YcvBoGhBI5WSlB2y9/C2j3yfLtigLhhnW9ZoeXhYySHTNZmmmXVBW5VMV0S/p23nOB5RtGE/8Hx64bsPn3n7ds/Q7fAOrBdyteTNeVBLomyQJHlFC9aCGMG6jG8dfd/QDy3TZafMAY08UaumBvXW+Yp4UbufEZ1mwVHR6daa11/rYSzbjkO2yPx/+nC9vXnHr756Rz8cuX9eKMYRJBMMlKbSHhrmQQ33tSrQBaO+MakwdA3eFFxJ7Hc7jFFqkYhuAHNRC8waExEhiZAzRAKJZ6o3dG1H30akmen9AdPC/qoBX1jTiZL1RTrPgVLPWOux1ZGzx7mW/bDHuqoidq3UsiBlQmrSNxhNqKxz5k///pl/+fc7piVR2Hyvr4erbCZiXi1X9ou/VQ9dRaSNQ8vXb4+Mu3aLhOiLX3BMq+PT/cLzydJ1N/zq17/l7/7L37DrhRROON8wx8K3Pz7y+JKpdYAUcdlgc6U1Rn8Eck76AabHRkPbFJzMzDHReE/fDDhrcM5DFlxqKI1QTUdJBwwRfRBvE2FJ2Cq6sClQssEXeNf2PJeZvnb0c6WPQp8qbl6pOSJNYSnCEiLXw8BX3+zp9nta57AkSl4pUnEieKvLj+QsJgiubGyKZNUmRKRUtb6kUpA0M+wcRkZe6sK6BDWAm802ZCqJTC4V65LOeVnAeIy0FCt4U4lr1Mjsxg21WfGBIp1uok2m+oiRiiES3nX07U4n/ynTJIdk4f7xwpJmYqNDQdf3pFj5/Pkzx8MtJVXCstINLV3XcnW8wpoLYVmVQ5ATUiPOV3wjGDGsq3A+wXQxONdwdTVyfbWj79vtM+ap1RLSyrw0hDCxhpWuA994cgy0bsTZjhIr5/MZxHDY7+k7ixCpAToSXx86fvf+DW/f7dn14KvHloZXxoVBEPsaC836oC0VYt0cSFb9oqZsk5siH0mqQ14fdvz+645iVu6nCsZSuh2H4xvyMuOtbG4e9YdWnC6iUiLEQM5we2sZdgPLuvD0fOLj3T3XVw3HocE6g8uWEgslRfKGyBTZFlziyWKBgHOFpnG0nWMYBpbdSAoTKc46ob8Ggiq8kule7VfqDHBqF8tR3T620VuqVQipniegTei/4HD9w9/8V4a+5TwpxFq8JW1pi5JX2s6wu22Y1qIcUPTpXPNMa1tuxh5DJS6VFPMmvJcvVqRSIktYmGNBmo4khiUFUnPm+N7zzTfvGdsjscxEJmKozPHC58cHztHyq2/ecby+4ZGZaQrEsrKmC+dVFwFDOeLsXpcGyj+i5hmpcXtV9QmdY+XDhxf+4Y8f+Pxw1jf/9UQ1+g6Uol9QqlE2gDGbH5AvYrdQuB7bzYJlNwDEprZWy/MUeXiZwTjevnvP3/7dX7M/tKT0gnWWSODD3QMPpxlrD8wOwnAmuqLACgN9a4nVkmshFpBSKFTm04UsK+Ia6lrobwasGAKZvKxUXwg58ryeyCXRugwOnECfK01MtGtkdB196VhESJIYxj37v1LbzC2O/TmRp8R5zVzSxFkSiY6+39FYz+PDZ+rzC4LHm4SRiLOqTRdUCmLTKp3VaWOtinrzVWHILmWMMZQSCcExDAes6Xl8fCSnvE2rBbs9uIxRipYzhpiF1/x3KYW2balDT1izJnZqUupUVmkqZpS+dgGXC0gi7RM31sG8Ep4n0stCKY3CZ2pk6QLWemIy3H1+ZF0ieVuurUvgcHVg1++YpoWh7zFSNR6MhixqTdTSErMwz5nTSyUXz9XVgZvrkaEzGKsatrUCWIz1iHQ4W0le2RddbzDdgDU7Gj+wzmeW+YVYKlLUzuasg7pQ0oJxDccDDDbgSgN4qpTtkPgL8E3O1FwIIbDMiRBWSsm0bc8wjohTeI6ISgYYpcr1XvjmzUAogn+JnJMg4nFfXyNxJK4nUtWkmkglpkzJOmSJrawhEkLk6urAfr/jfHrhhw/3vHt3ZBxapd85swVKMjlHECgmKdjFGhQ077DV43xD1/fEcc+y7JmmZ2JY1P0julSsVT8nYsrGVDJb0GeLutZMLn+R7JKE3RbD1jhq/T8fof/h4eqcbhTDqvaqLIUYVnIK5Jwxe+HmV9c0a0N5CkzrHWs541vD2Fv1QSbRVFaOpJJJOeOMxVshpFWJTMZsUTsokuiPltt3HbseSIV1sixFiHVlSSuhLGAcKQX6oSFlx9PzB3JeMZJJzUzXXNM0jmFocc6pj81UnZareu9y1mvp+SXyT3+649+/vSdXFc8pnlKUvlSMmi7qK5WGzVhc9Emmp6yhaxxfvd3z9nbAt1BqZj5PGK9A5/vzhaVMjNdX/P6vv+Hd+5GcX0ADxHz4eOLT/QSmYQ0zsaws5on79ZGxOOXhmkwslZALuS5ECRTxm4PBYrJwucx0/cS4H1lrxkgg5pVpurDEFwYrjNXQi+UonlEMTax4wLcW5x1HV6FGci+8eb8jV0PfDRzWjFsKrRu4iyv/OgWyKIQn5hm8MKeZVCJdZ/A24wx0rcc3DbZRKIhZoY2O0Y2cJGDCop5XHCIJ5wpeGkyx5AzDuAMs55cTMQakJsQknKlbgshQEazVrTFGyVi1WjAtzjYb6ahTlCSVVJP+f3LB5pacC1kSrvE4b1iWict5IT6tSBZK8zqtGUq1PJ8mTufAMI74tiPlzGU687besh9HHh8fcV40abSkzT+qGncplsu08vi4crok+l3H7e2etjHAit18yLJR1KwxNF65HH2rOmCtM8iOvm+oNWDcCrZQJDItgcYX+tazroHzZeWnp4k390+4ajnswPmIOIMpTkHjGz6zrIWHj0/89ONnwrIiUmhaSz/0xJsrjrcHnFHmreb7HY0YiIFjU/jNdSWlQr1UZYB+QAAAIABJREFUaqPLa8mOxSSeLxfWZVU9l0bDCtUoLS9WctIA0DgOTNPE5/sXvv/hjuvjwLHvMbZgbCUlTW4ZhCwRYyLWeuVoINRisc6r/a3r6IYD/XDNPK2k5ay3lA0vmnPGUGFjwGKcel3ZgizG8xr+KSmCUf01bwzoX3S4plwJIbIugZKgkMhp0SdPbanVsL9qaN97TtOKKQ1t6RFbsWKJ0VBNC/LCGrU2oWy2rpwLS1C4iGt39Ls9pmkpzUgwT2rzenGcToUfH5/JzTOmy2RZKBLoshBDJEewtFzv31FJXKYnLpdPvDmOtNctQ6d1DsYYjFhFhNVM2fifIWhg4B/+6UeeLrrhFhH9MsrPrNbXWoec85cP1WuEtQo449j3Db9+d+CwV+vV0/OJHz78QEIoTc+nl4WYG3719re8//pAzk+UOoMRfrx74s/fPXOaDcZVYgmU/ERaP2NK1NtCSlgMY2PJWSgixFpYC6zRYCl4ffiyxIlB1JQ/LydMqfihcGuFo1TeimNvWvau5+Z4pDOWGFZWUZSbsZmSGnJ1HA6CkDCyUh4v7KYZs1SeTo7zAmdJ5HWm8T12aJlTIlMQ71hjgRxppkw/wG4cMMaSayblxJMJ9B7qChQhJYi+0tSKZIfUnkImcWLX95BHLmedvpX4W7flj6MgGnWUohUwVGr1CM2m8Vty3WKNJqPwMcOyJpaLCkHFCn7wSGOZzwWpLZ11pGQxOVMXvSHNCKfzCqbh+vYtznu1Za0r67owDgN96wlpwXlHyh5ipBRPCAvLMiFY1lUowG70dIOlkpTYlQUnG7i96pZcasaI8lZTXrDWMewaSp2ZlxPzfGZaAzEWpvPM6fkZK4Z1LZRUcS8B+fNPpJj43VfXjENDK61GU4u6M9Ia+PzdIx++/ch8WtnvRoxJSM3kmvh4voNiuHl/BFepziDVIbXiqnqMTe9YDp4lTMRaKclSq9B2A25ZuIQLOa9gWoxtcE6bNkS22wwV8YbdOHI6vfDjp2e+/urM8HWLEwNSdClYRHGCJVNK1AefOMQKxjtsdljX4JuWfhgZdgfOpzNpncl1cy9RMBRy2hwSojyOYoAaNouWDn/WqBGNslkCgVp+oeYaVpinhU8fH5jPK01zJoQVbxyxJmJ5onhHczWSh5lsoUZHihFCorcTpk2kshBSIGWFIpRaKaya+xbPYbdntz9gnENcz5pauskTouF0WpjThSorvtFWg8Z19F3P2N/gOPAyn0lxpe9aHi6FKh3jeMNh3LPrW726vF4DStKlQEmkDA/PC3//Dz/wxz//xNPlhCA4t+WrX2OtRraOHbVa5aIf9s2YgSJMLLdXB27fHmlag3WWm9sbumHku4+f+Z9/+nf+/NNnduM1Rjw5ntnvHV3rwVl+uHvm6WIIoaPrO0Qi6/KJx7tPuDJgnCPEpHahUEAcjd3RpUw1QvFlMzyDNWBrxqWAI9DZwu3VW66vPTtmxloYraHzQtM07I4jvutI88oalg1RGIixISppGmeKxkXjgl9X8hRoViGfDTMRayulVyi4q6qnGdsi0pLSQilg7Yh1O4wUcgkssvKSVm7Ekp2jZEuoFV+FWMCmhDMrXWMhXUjpkbYdCaEi0VCqoYhBZ26nWqxUqing2czeWvdRSMrgEMAWjDc479V4M61Ugg4U1mDGjmIdORZa45HekZxoVZDJSFCaVpgyh/c3vH3zVi1ZIizLwul85uo4Yr3buMdgjMe6QeWfVFiWSR0QtmPcN5RaWNfAru8RKYRY9EETZqbpievr4yZpLFQyzjX03R5jCpf5kWmZmJdF47bREkJhPilnNyKYpuKXQrg8sHfC20NP37QUY6hm22kVYTllnj7OlIulNyOuqDfYmkpaIayJzx+e6fuO7rqFajSIEDPhspBTwLUHbvqGpxGWc6IaS4gaOz0eBoTIy2lhDQulBGpRO2a3O9C1G3fZVNqh4zJHHh5nvvvhM7fHPVe7Hu8NBkuMZZMzVGrh1Z+Lo1bB+YaSEyn2tF2g3+0ZdjuVTkLWg9UoXF5Zygq/FnHU7XxCPMbaLxAbnXYdpbzGYP/P5+d/eLj+P//9n5kuz0yXZwyG6+sj79694c3NFZ+fPpDKwunyxOVhZTYOOoiiFq2mGFoTSPHCy/LMsgY9gqqodUXAGk/fDhx2I1YEyRUpDl+uyXNlCWdinZE2UR3U7QrinTD0LTc3B2rOPD184nS548VV5rBydf2Ocbejaxtar8V1Im5boi1UidRSWNbCv377wP/4xx/4fDoTcsRWq6k0DRhrgqPIX9iutoPWbmECQKqj8w1vbw+8uT3gWjW2Wyx9P/Lmfc/1c+LHp5V3b7/mt7/+DTUvPDy88HKeOK8rsRhC8BjrmdZMLgunuzvSydLbEd8ol3POlbUEshiqZCyJtm66WYGdh0MHx53j7Sh8db3jzdWvOYzXkF/wocVZ1fQsCWMNMS4KHk8VU4s6N0LEFAUxpxyhZNWq10iZAnXRyaBhRVixkhEJiLR4L5CtxhGNA6Pxzq490DX9xlhw2CjMYeJuWWlWweLpRYhz0AoSV8HqBKy2MShmxZlXK9arp9JRFd2OtVpPUyVvZKeKGJWB2CSDKhWM08Ud6gn1jdWFlDFY31Io5GmmsxbZWcIS8aUyFAulJ0kk7R1v37+j8U79oKUwr5Gn5weOxwFrPOsStsWoXlWd6xl6QaQhrJlcK7koZORJTlCh7VpKrop6jBkkk8pCTmqNEiMMwzUWwzQ9MYczy1oJCVI2hKWwzJUQNMq5lhkovEyWGjLzuRCXrEEUVyg2ITZvycqJFAOyuRBWG3Cdw5iGsljyIrykicfDI18fv8JmQ5gznz/c8fnjj6S4cPvuPcdffc3bq4aXNLHOgVq7L0nP/VXHbtyxLLBE3fj3XcObNzfsxk5h6KgDwPmW+bJwd/fM4+Mz+97jrPJeZWMOkDMmR6ozX8z9xhiq1c4/1zQ0bU83jAzjFZfzmRAitaxfbp4aJ0i6nHON1hIh1KJyqLz6gNEzXP9tfnmI4Ntv7zEy40xlHDv2+4G2FZybeP/uwDRd8fH7M0+PMyU7vTqbTPGFWBKlnojryiVqmZ+XihhPMXqN3jUd47CntV5vakYBMWGJLDlz5oWlOWOagm8dTW9ovMWYjPORUi5o3DmyLjO+ywyj52o/0HvPrtNUjjEOZ6DGGdKi3sdc+fxw4e//14/88HCiGNEyvGwoaPSy5KytC1vczVmrtg1R24p1as8w1bHrG755f+D2qsNZfdEtAjUjqXJ7eM9//due4/7A3/7139J1lvM88edvP9JcEs/nibCeMUQal7lMifzS0ZYGI9q4mYousUoVco440S+er4W3+x274cDNzvNmbxg7OPaF3Wg5jp62mZiePyr43CtvoBLxeZvsktK0MMJyWchBOxtyBsmVHFZimHApojlUixXPQSpjmVVOiZVgA2UrehQLrjHEWjYXwBYpLHpIWNsQfOS0Zk4lc0yWwRjMVjOiy4lCmgEpVOOptuKM1bLCLcYq2kjHBu/Uq5xxGOeoeKrKaFSppFJ+hrznvBVW6t6ycUJwhpoj+eWEOc2wBmoytKI0g1wKvlZ23uBvBkxXWOIL1h8oYqEELtOZnCu7fkSqsKwrqVjWNWGtpev2tO3hS0/XGiZiXCil8HKeGTI0jQMj7PY9QiWEgKBsjXGwWGOY55nz5cwaI6m0xCCEpTBdVkLQsI61UOYI2ZAXoUaPra3Ggkv+orNW2F63SM4TIa7bJj2zMztoWmSN2HQhpczT4zNXp5HG7/j03RMfvv2JvCZdOJZHxtsjx+OBYy9MawDbEErFNIIV/U4O3tOLw1pD3zX0XautABRKUu1013nS3PLyFPl098K72yOHXa/R9gQ5qVyWbcV6C6b5EkNHVBM1zmObjrbfM+wD/fnCMk+EddGhYdNNc04U0UWpEatL9y2OaI3a+1TrLipB8Vob8wsO15TAO8O4HzmMLcs68enTE/uD5/b6t8Sy4/w0cZlnSi4bzs5QnRBzIeakxtzObZldo/FYIwxtx6EbaJsGYyumClIzIV+4hIWnknh2F4zP7K97up2jaexmEQk4l1nnmV1/pG16UgKTK8dux/vbbzjsOnaDw1m/IcMSKU6UFKgJpgv8878984//9olTWPVqVCzY1w2ikL542V7dDT/74kB5eEaUznN9HPjm7ZFd7zX/XwTtyDK0zY6/+e2v+b/GnhQCQ9eSauV0Shi3p+0q5iS0xtA3hqbJ2DBR84kiyrIMYWZaZtZ5hrziyszYO4axZdc03F5fc3U10rpCawOSL7Qm4ajUGlnnhTxP2OgwpeCGQs4rpYCjhWp1qqtecXwlM80T52ll37aYWDGhKig4F2q5MKSWawMfqiNkS1oCl2kmloq1hnIwyGa/ERFKTuSs9Ke8quYqRgsGgxFs3+NrReKZmjJZtodTLjhvqdbSd56C1ZRVRt+jWjV544xaiYw6IQqbzi7be1gNdjPDUyySVW+T7UDODoLJrE/PXP4lUT5NmDmrYaRULeY0hmSBRh8kaV3oXg+LZg/AsiwsU2Q3jIzDwBJWqJV1mVmXQNsoMlON8QZnHKbpiTERQiDGyzbMOB04IpC1PXYcO4TI+XJimgMhWHLpSKmyLpllzqSor1frPCkGako46ZGkEJambXGdGvitsZsXXV8j5wVjC6UGLBYvBi2NHDFjxSwRs2bCSXi+P1Nk5qc/P5AnwdBBdqQ1M7+cORx3vN93zOvCp1PQTirrsL5RyFYG40QtU43DbrKOiN2u2xbTOkKfOZ/P/PBx4quvJ9re4b/4crda+6IV8WZLp23Wc0UTFmULuybTtDu6fsT7hqhvCzkr8lpZtlprX3JV4pWRL9a0Kq/1S2y12huB+5ccrn1nOByO9J1ljRPLRessIoGcnihrociC2EDJm30BC9ZSjE4rr9BwqoViwRha17LrR1rXYa3fcu6wVseZyrmZeEork1mxMeBCxPU9rRlw1lFKJCchrBXqzGU+4RrLrttz6L6isyOtMwzd5k1DEyM5rVqpHRM/fpr5f//5Jz48PZNyVCu68UpUKhpxe63YVl9upeSC20zEMenkZIyhHTxvr/d89WZP5wxWZFumgLiOw9Ub/HCldPXgSBTuHk48nCOpGpb1TM4LjTW0poc44xZDn4y6AySRUyCuC14SN3vPsfdcjw27xtI6S9sKY5fxrmBNJQatDJYcKWGiRo1HVpMwJFx1m46UqSSQ9MW7562BEnXxGFdCRWmt1au1LAdsWGmi1kqnGMg4QqlclkgoBdNaig9YZ6lboWOIEVm1JDGtmRxW+s7hWs8UA88UfIzsgabo72sAV7MCQxByXMB4nNVlSs31C3HeiZBRwHbFb3qg5ua1zmQz9NeKSMEZBYfkAhjLWjI2JuQucbmbqXNGkkGcKFazGpIpVBuJvjKVlTzNSO1x9USDpfFCzcJ0mbi5uaXtepw7kbK+tueXmZd0Zn9o8d5oUKZkfNMiWMV46Fi5La5U0+zbjsPYUSVwns6cl5UQK1SFRse4UlIkztoLthsdfdNzfoG61aLUBkxrqL2j+B7s1vpb1XIEmbbt6PqB2UVIWle+BOHxkkl2oNtfIemBGiqff3pWjTM4Om9JKVFwpBAJy4orhWPjuekbns8Ll2SoxmNMQ7ZJH5xSVbJha1zdmB2N21wEVej7hjVYXs4vfLz7zO11Q9N32C2FmVJRiaeU7TuqPloNTxmsc5tzoKHtena7kXM/MF+U/CWChidEuQoqB2Rtxt3aCJQvYijSUI16fbc40S87XK+vHG1bSWUiExDvsN5gfIHmhPdwuIYYtdO7ZKN5hVoQa/VaaC1x0a2+Mdo33rcDrevxvsc4q8sxqVxYubSRdSiY7JElYLxRmk5cyMlTZKXUM2YV7u4emMM9aznz/quv+d3X/42b/Vs67+hbT9t5XpmMVI3NlZo5nTN//Nc7/unbj8zLxvLctn55IySVLbmyYQW+kHDEWs1cV2WRUhJWBt6/2XN9bLF2A1sYoYjDtiPteECcJYSJagpPzxNPL4FUHNM6M4cFodI2hsYEvK34DLEEsqQtT61Iv8Ou493RcztUBpsUW1ITbSPsvEYrS8l4a0gxIWEml4iVSuPq1rOeMbnBSUOUQMBiXnuCasBYsHlh14I/9JAsJWZCrgiWlo5aFYAScmJeF1Yi2TgqLZDJ1RAjrHPQHFQpXKaZkLOm+aJuepOZsQ6eJPJhecGgCzGXKo1UbNGOq2IqlMyynDG+VV10axd2sE2BiSpb60VUGUDYvsT5teZbp1ypCSlZ0z7JkoOwrJESCkQh5YzuSIRilTufBFabubjAZCPZJJwT8rIyxUe8VNw4QO2Zpkeur466dLJPVDthxJBjhqp80XUqG6xl1Qod22KscDh2tF6QUjFYhn7HbuepErX2ZVmI26FSEsRUCbEwXSKnp5mrY6/QbLcjJYOYlkwm2ZXQNtyvkZco3JpGH1RiyBL0FuM7+uHAyWsjBiKcpsAfP/7E5Pb8318fGDuHDWBXg+8q5saSauZ0DtTFUKunRnClIB6udg3HCyzVsoojV51QvVOJrVRFFNZqYHuQGWs0AWag6TxD6lnmlbtPTzy/v2Xsxi1MUrDb8JNzwLqGWjzWWbw1W5GA1bYCr0S2YRjY7Uam044lvKB+jaQN3BjdtgjbzVSAvLkCNI8lZIwBsZUvMKT/7OHadoWYZqUSOeibStc72q4i7gVvDbfvjtTc8sJMClUN2RSwwm43sN+PPHx6YsqBnR9oSqM1vjic71TQl8BsZyYXOaUT/VFTRUuesN7QDQ1X13uGrmNa7hEm+v6IkwNTOrE/jPz+t3/g6+Nf0fkOqYm2BWcFTV2wWagyMRV++LjwD//6kY+PF9IrgLpsT88vFdf1C4ylbEFkMbqNFKMJNAVeG8Zdx7s3I7th21hbfWPEDTTdiLFC2KC95zly9zgzr6Ia6rpwOZ3Ic2S37zkOlb1t+RCKpmNqIcbCGgs5BtV7BRoDbaOw3pIzhqDSzGZ6NhgkbYmm6vC9V/1zjdQUKckpbJpKypnBj/iysqZANQVvE2w2J9935MbxnGdy0Ek2iBBECyCnfGYyGWsbfQpFzcYbRDUxCiXDdFmQedEFIQ2+WWntCt5R27It6zxxA6uXWijJEo2Sk2Szo4l1avB22+BQAVPJopMQxRKKkJ2a1X0xWvFdAyJbm0RclSKVHSk54gpLUE07FkPSM5kklUhRKpPAxSaWNhGM0pNs35LiTJIZKw7vVb6a5mdCuNB3LW3XssSwJU4KXeMpuTJPKzFkSoYcCkkC1oG77mmtpWt6+v5I2zhiPHNZL5zmmWVN6iRYKzlBzIbzKXB6vFBT5nh14PpqwMrI05NREhkKVcI1PFwC33+65+2+ZdcotBx0grDOMexHXHchpIphJcTA41L4uJ75zVHbjH0pdNZR/QxuoaHBB0teoWBJISFMtK7hqh+5GXue5oWUK5A3H7Lo1FmF6oRclK3wGmW1dtPpEZq2I6wdz6fI3f2FN7e3dFZ1fXktESiJUtdNZ/ebvUsXUdkJxRmS17qiftjT7fYs8awgmKKHpLPN9oFyvNbci1R97WqhStgcQnrL4C/4zv+pwzXXZas7Vkiwc+BcQtANsnMn2qHj7ZsrOhl5eb6wxkgx+sQ6XvW8f/uGwfc8uTMuecpZ8V6u9VRvSHWhdJFgA4/zmeQz12MPsx6QbWe4OnY0jbDGM5XIYb/nN9/8npfnhh8fz9gAng6L2SAf0LbKMthot+SUSTHx/Bz4xz/d86fv75lX9d1u1uEvyDXgS7SVjSIl24dPyUFmm4AMjWl4d3Pg/duBptEvTxEQ2+G6K2zTa9FaDSwx8ePnF57PlZwtkDAUSgzkNNH1nuNYWO8WXu7PhNWS1kQMkTmuhOVCe1rYe8/SePqm1QdIznolK/oEFxTpKDmBqVvFctaFw5wVu1YTFMhl4jRH7FVDZ6GuCakemzf5JUZqXdUqY4UXIquFLIqjWUkEAtHZL4jKUhRsYUrVjTTKiFjCRZ0DxuLdhW4odJ3FO4G9QXxlmTQgUXKhZksWT6oJk+oXu1PJVfNQ3pKKOiWyoD7F6ki5cn8+czGVN+MOLy05V3IK1JzIsVBCIherB2mpWk9TK1ksyaiHuFT1KJSqRKa1ZIJJlO025IonxcQKZAovT8/EaOn6nhAD83Kha6/p3cBEwJqZpnGUUjm9qG0KY2ialmXWv3PftVwNygjY7UaMc6xh4nx5YVoDS4ysIRODTq05O06XhfPLTC2Vt+/23Nz2DF1LLS0iat1Sw5rH+ZbVGj6cz3z9/MTtvlVWA8oJFoTxZqQ7npmWExKVSVul4TRn/vzwws3XBjGFJcMaC4NrGboRPyTWywV1xBdiVXpDK5k3neWhNaQ1EYomHMloIrNknHNqsTKGZhuIrN3sTiJ0TUfwA8vyxN39C786zzRXHdZCdYJE1V9LhrpV+lRjQVRDds5RXMZZR+NbtWUNey6nR13esQ1L8nM7SBWVS2RrMqhGtnRdRops58EvnFzXGPQQqbrS0elP45yIIDbhJWGsJblKbA2pqgYmUgjridOTw0tPZz3LacFWw3BoMQPMnGAo5D7yfLnwuMwcxhYrqh2OO82Mp3hmzo5CwTVwPFxzuLrm7vGBy3zGUlmmiadyT4miRWe/GrFG6ViCJcXENK3863cv/P0fv+fTw4kUFYT96mH7y7qW15/XUsIvkJa6cQq25Ezfer55v+f2ulOqlEHBH+0VTX8gVyHXSKqZH++e+fDphZgcrfMKAnYwHlpmc6EdEut64cP3J+azI64NITjWGJjTiVAyL7PwPEUObWVwRonuWZtydbej21esA9MiriDWE5ZCjYUcKlbS5jQw3F8K//Dxwm554ndHyyGeMHlbYmSDpEIKM9msxCXzeSq0psFF7Z+qRKyI0oZKxorQ9A2msXhvoGbCGjREQtEKkg72o3B91KVLro61FtYcKXFWwHa1SLKI12KXWMBl/bKklIhhpe0bnNPK9FQyUrWSvEomSuX59MJe1MlBLdScKbES50pYDKV6UlW2qLGGvhW6RmgqxADraqnFkmIhl4ytiu5oYqXNlXkOBBNZpXAOiTVDyBf2V4Gu88zLQt9aWrfDyJm20ebXl2dtJ0XA2oprqrYh5MqwU7fLfjci1nCZz0zzmXVNLEtmjXr72iRSwhqYLhPeOW7e7Pnq3Y7dAK33xGipkhFTcK7B+AYMBAPPBT5NkT+USkf9AmKpUmlGx/Htjsvzo0pFKNvWUPn4PPHx2BJsy90Fnh8Lb4+Gw5h4a1uwM7UUUrUkY2h9wMqZse94ewXzY6UElevwBVmFdYmsKTOIp228Hrxsko81lALVGnzTMi+Gz48v3N0/cdy/w7qCKWCikPIr7/W1ukU96FUEjMVYg/cG3zjarmcY9nTNQF4nygaqkeKpoo4hcqLWSJWCkRZjNbXF9hCv8B9Jrv/x4cq2PQMduVPaIpbGaZW2NEjpWafANJ1JadYkD0IJibUUXsILvkbCJVNipO96ahNIPUhnKF3h/vTIw3nWJdU0s8wzxhV8s6qGmCGGzJoDTSycLyOPTzOny4SYgrWW83wmLg2uduyP6nE1WKpRcPA6B376+Mx///++41+++8hl0Wvh68FZ/7dX6S8P2L/8tXIGdHq31nIcW755f9SHghPENVh/wPfXVONJcSaXxP3ThW9/uOfu8QUjhsNuBzmzxplcF1I98933L7hLJjwIsVjmUjiHyhyCVo9b4ULLwwL7S2bnIl4qjWjvFxmM9dh2B96DiTR9VdvO80qY8ubjy2QfMa5nzge+e1mYn+6Rbwb+biw0JVGtQ6pVNkSIlFIJ1TIvVXmY0myLJENv+DmkgS7InHN0XUNjhcnU7cGs1ribY8fNjcIezy+ZOaxMUX2X56SwHFcFX0R1VLL+HZNgKuQUCVXxhsY3SBUtAywObYvNHPajXnGN57V4TqlO22K1GvL/z9l79ViWZFl6n20TR1zhHh4RqXuquqeBBoYcigb4/38DMQOSrUqnCOHu1684wiQf9vGoniY4ZNdDPkQmIuHiHjvb1l7rW8VTq0Vk02yNJnZ6X+m7xjA0dtUTU2CdK2squrRipTfCtRSuxW9xVgDD7aXy+Djz8HZgjUof64dB4e1WiWI5Z8QpDcoHgw+WGAvONXY7i+u0lHBebkzLSikV73d0NRHTlc6qF1bE8HKecNZzf79nv7M4u+Jsw7ugCclWsc7hOocNYQvEGGjqo63tz0tbYxzVVCTA/X3HyyFwjgZXGkeb2UnhtlT+eK187oRPN8N5SvzhOvHQO/63779jsAFFavuNPwUikSE03u4ML7dEjLCiUs+us6y3Qmps8eRKyRo8MLXooa93ErxX2WK63fj48ZnvvjkS9hbZuM+5tE17TUheQfQW9/r8vlZ2Owdd5+l3e4bdHct01QGlCtX4LXASN20emvFa+FkB9PA3OGgRBbb/BYdr3x8xsk1qrNtY5r7ULLQaaCVsqZdt6WAbpoItHb52uDLQMlgax4c7doeeGiLde0Pxhs+PihZLRfDSQVmIaSVYq4Dc5mm1I5dMyhljMpfLgnM3pjljcaRYOZ0v3O+O7O46DncdfQi0zYpTUuJyvvAP//Iz/+Uf/sjzRd9UtEppG+fj31gq/u0k++XQRTdcBou3lnf3O759/4ax94h4jOzw3R3iOmLWBdp1Xvnx5xOPL2fmfMKJ4XS7EEsk50RtC5fzM8+/zIyx46070PzCMibmtlB8pmfASyN4oVG4psxLApsMBxfwWxa6mR4JB2rncSHTjZW6RnwwmGBoEoGVWiK+et6NI79+OPCHn3/ETB4zdojJG4ZQDxtlvBqcC7zd3+NiZU6JGatb+Jq0p8oo+q9s3kChMA4D3u2xTq/wh53j7Z3F1MjLuTLfdBGarQcfuFr/ajmbAAAgAElEQVT4Y4r41Ng7izSD5ILVjhZM0VtULoUkiX7nsTZQm0cJ+hFrKnvr2e92G1JxcxqUoAYCp4zhaiwtQS11A7uULd3Vtq6rSvBCFwzjKOTqiaWqLtwKUxVeYuUUM9cKn2PiebkxX26ktCPVQm4NHzzeeax1dF2H954mGWurkuNsR/YFK42xc+SSeL5o26s4x3G/p/c9KWthocWwG3pKnfn5l88sEfrRAZGcEtL3iIyUqrdI6xyh7widxVm9ZTjTyGnVpecYvhwYRhLNRcLBMr4ZuJ6EUBJvQuLNHh7p+VwNNlYWL1xDZrlFUiyc3iRGa/EtI9UgqccUj6kRUzJ7a7gfGtclU8tIaZ7eNTpXSSmpTFUyKa0Eayim4bzTII/R5VXwnttq+fx04enlyn63p7MFcRVphlqh5KiA9Q20jrze5rRk03mPC4FuGOn39/jLbXNebAlSEk0SsjlkjOnR0IomI03Tg1bkNfn5FxyuPnS6+ZcMYsg5bX9FNgbnnnG8o/8qMO73nG9nrvOVGAt5VR3UoRG04aHjq28eEF+pwSF74fQy8/HTmTithM4w9LDfCeJgXTLGBGiOvhvp+ozUSPAe5wbtGypNjeJGK3TH0XI8eO72Pd7pFVHEMs+FP/30zH/5x5/4+HyhNjXIvyqs/9YH/MpsrZvB+s+Hqy649E+Wzjm+eXuvkoAXMAHn9/gwUFql1kQqhc/PE5+fr5yuH0jtBe86rmtTvmnOGFYyEA4H6rXyuL6o8vJmhPsJW2akQCuevrO41phvlk+lUFqlVMPRhI2Kr5U2zTTCGAhDZl6vOpk5hZWXmjduQuZob/z9t8LfHd8ziqeTvMnMFaoesE6gZIOrwlvvKTVyiytrsaytsJpKxmCq0c4yo1YnKxXvDM4pUMM74Tiolet0KZyvhpotXhydHyjNscTILWkwBAx/Zzq+bgGXC82BsYYmgstCnvV7MoPW+ZSWaGXBU7DNbNFYQHTbD6Lci1CwRhsQdEmmDoPaLNUYWjaktSLOYLuCMgrNxh7Vq6WtFpPQalygLgsuRwYDJi/ktLAuGifuuoH9bkeMK/vjgTUtpDLhpHB/ODCEI04s1uiyq5QMttEPPV0/EryBNtE54bh/oLMDcZl5fDoRlytGHLmIgohyRcyAMR2lak2RWIsPwtA5BYE7wZjMuiws8wJ3e/U5b5g3IwYThOPbPZfPe+aniTvvOR4qp2ZZXcOzQmiYZilXz5wK1yXC3uNFr9jB9FiCeltbJrjK3Vh5uQlx8uQk6u6QSEw3fN5RimfNDZfAG4NxgrNa8ZOLxfsOzMzzaebjpwtfPfSEQV+ERiwUfe5aM9SmNwWr4zPGWsR7bOlwKRO6nmHcEYY9y7JS06IyiFhM89uzrqB3lRg0mKG2L2WQ8JcWFLaaqVWRcSJOx3MjmnoQh7M7rO1wfad+t84wloG4ZtJaIQotQguWfh8oXWQlYkVIc+V8nhHg63c7xkOlG0cwhtNp5XKJGBPpekvXC8439v3Au7dv6bp3PJ60Dtt3jWAqu53n7jgy9o79ELarj0J1Pz9e+K//+Av/9MdHbos2DKi3zvw3RYNfIC3bgWr+zam7mXm+pFkO48D3X7/heAxfuAi+39OMUDbv7PN54tPzwst85tPzTyArIQzq+ZSgnlhb6Iee+7sj8+PEj7/7yFoqx05orpDaQomJeus4n4RwEIbBccsw1Ur2hVwS4wrOeGQCiiH1PUlgup1pq+DEq+WlAtKRq8NJ4ugTh7sOKZZlTmqJ2bB9RtRMnRFcAVthBUw15Np4qZG1KTVNZOsxahup3TSs1ZrnrofBG1wtPJ4TT6fMLbNJKQ5rHU6Z8iTreAL+OUUG4xmlwxedhJup4KC4RmkJlpXeOiQ0RLSRQZp2lmZjtMvJ6AbbOI812jUlpmCpqg/mRM2N0oJ2gGExIir/SNNJ0zSut4lcDcYHrYkvldYcvhk60+ioJJsRCjUuLLeF6Toxvj9yPB5Z4sphXbdCw4pthfvDHe8fvuHtmztqy5SykmvEW22HMCgSs5nCbufwLpKWmafnZz5+fmSNBeMyUyy01tFbwbkDNEvJiUbFiO4wvDMbutJCWpSvkfO2oXe8Fvc1YzDecXhzx8PXiQ+XX+gqvBsqP80zEXWplCq0VchrIhlYUqJuL7DgDSKFYgrNChWPKYldJ+x7w8scoVVyXbktJ06nF9K60u7uab3q/YGidDRb6b0mCXPWosXreeHjhzOX7+459D1WFKxSStvOLdGQiLQvz66xmtYyNiPOEUKg73uGYWC+OtbMFlN2gKNtWNFSE60lfels/661TC3/z8Hs//fhaqRu9R9CK/rmb83QmiaTgh0V5Jsy023icnlmjQvedwzdgFhLddBKpdiV6zppZntuzKlhSuSrr4Tj8cgw7pnmxk8/PfJyMuTS4btK6A3YBWMW3j4c+eG7b1jTyKenn7FuJvjMaDu+eviK4/iGXa+b0lcJdZ4jv/n9n/g//ukPPL9c9WBlo121hjNC+6K4/qsai38ztbamCLvXfghvDA93e779+o5xDEgY6cYj4ntSLdTamNZFwwLnRx5f/sj1euPN/QNDP7KmF/qu20AWkTUtIAnpDcV6anIszxHXGdVPTSXXzHzOhGrJh1EXPR5sWJHzDbs6YCTHTHWFFB1pFJbLgmmO++MIBtYp47Kj1IDB6S5ZPMEYrGj19Gv0UfvlBcThqhCsIReDacJM5laTXl+BumnSGhEWWtOJcugszmktx/l54XyNnNdCQg9ea1U38wJD77CLYW0wWfhNWTEV/sb23GORlrCm0nyjoBXvMk+4UpC+UWwji2CN6sXVNJoRrO1AempN1NK0Z8oY1F1nWZMwR0OOmqoTA2IaIXgOxx1VYFoiyy3BmommstTKYhp6gbR0YpmopDUSp4y9C+SkCbB+6Amhox8G9vs95BlKxtse7z3W7cBUrlNlvlx0p5Gt3h6sox9GhtCTY+Hycubz8zMv14V5rqTadFljDaHvcXag1EouGgxxrtAFgwsWJ5YWE3W6sXvo6cKr3UgP3rwteBGQznH31R2XpxP5+cYb23FIiRuJtRhInjwV4pKovaO0TDUe8YLvGsatFFMoTWEn1hiCKxx2jf4WmVZN+83XK8+fH1n6hKWjJcM6Z5ZpZTf2DEPY2mI7nBO6zjGJ43SaeHq68f6uwzply5qtOUADPoArOmQh2pLrnLa4bvJA1/fsdjuuL4H5VnUoEhAjWyy4QksYk7dhLX85rGtVBOVfdLj2w4AucAq5aIrBilc/n3is9JTVsK4L67JwOV1Z1pnDHnoczRRiLuSWyS0hRv1qNVbW5cpuB+/eHzkejnz6lPntv3zk46cL3nvEVcIA/b5hzEIfMse9p+SFeU5ULnQhsfMj7w/f8/X9X9P7N+x6IYSw+VUNn5/O/Nf/8zf89Msj1Ia3Vr1ztWwgFtVSam2015Im4BVU/uVFY7TQTAMFFu8s7x+OvHsY8f2I7474fr/VYlRKK3x6fuEy3bjMv3C6/AnMjHf3WON4e/cVxzeBfihcTpHpsgFlVovJATN7ciy4Q8AdDTiPHwVTVkzOlJes3cCHSo6JMq/UGhDT0VKlxsiSV8pFs/NdZzGuoxsHllRIU0bKtnSQHbUqr4Dq9U1issb+jKGKLgYNWhlkRMAKU0ysTcsrVYpuWDF4p9aemjOtZnqnZX+nlxuPl5loHK7rKGuhRSiSSXYDgjvHiMUax2IaP64zL2Ulest/7AbuaAw5Y9eiUhWV1AotZ3zTxuDqHM0ammw0/S1jjjiwBmsVMC7bysVIBe+pVkCUE4oUuk4YR4dzeoANXcfcYJ4zE5W18zytkTlF+t2eW6nMTbCz5/wM9/dCSllti34ztvvAcX9HW2bWaWKeVx5PTwyjsjtcks11onq3FWEYesZR+8Cu55lPTyeenl+Yl8q0NHKB/bHf4NoNZxVEr4GRzNAJ/eDU1hQL9XTmXW/44d0bDmOH3T7bYrfmgVdCiWv0R8/bb+6o18iaPd5aYpwxUTDRaGtuM2CC2hqNWhUlWGX30pCth0uk4k3lMFbuxsIUK7Faegl4cXo4VtTelgvT0jhPM955duOOw90R6zrVwTvt5/r4dOP77w503mmNk1XbYSvQ3BZbFb2JmE3WMdvBWn0gdD39MND3PWerIQ8xdUvyyVaLrp8jmlGXxhfuRfsCyv93H66hH0lJ5QEjCUzUmupNI+vDgdo8FGi5sNsd6LqOsevpnEesQ/pKItKsY9zv6PzA4+cXuvDM23d37MYHfvzxwj/98888fr5hqIyj0A+G48FjbYO6cth3mJY5PT/ycivE5cKb4x3fvfkVX939Dbv+W8TAuLM4Z6kFYmr85vc/80//8ifmacGLYMSph7EKpW0xuao0+rptlduWNVd/+p8PWzF8uRr0wfHV2x3H44ALe/xwRzVembWm8nK98Hy+Ma1X5vhEY8K5wsv5E9Oc+OH7X9M54enzBz7+8sTz8w2DI7SBYAJzmlkmT0PorKWNQpOAHQImJ3xzBAx2nSm3lRR7kt2DHQiSkar1NyT9YOdomG4LOzvgvGduC6ZGQu3wzeKpaNXwa6hvm+dF4SfGWCqVWDK5NrI0bjmzllcoS0Wa4JzFW4c0rVgZO0vXWS7Thc8vE9dkqBaGvuEwmGZxYrTI0leMr9jO0btAu1XWbDi1xj+XFRHHXweLyxU/a7V19oZoGrUZ8lQ02RMMzReMU2InxlAl0SwYqzXJznUAWBO3QjpHbyw+NHJcyUUrnZ3XyaWVhkXwznJbI6U1wrAjLSuXeWF1Ay+5cQXM2uiuK/fTjUPsyOuV0b/BW493HRIai+/JUlhzpq0zvu+4LROlZUK3lS5K2w54j6Fwu954fHzi8fTMmgpGOryzrOuME08XhLH3BCekkpUA11QGWpZMWq90t8LbVvjVd/d8/XZP5zSTpMk+XfLaqjUoaY1Mt1mv9cOOp+eVW2t6c0mVXCNZQJzFGUuHpTcCbcH7A9Z2m1/AbvzTjAVGZ3kzBi6XhWKFr97ck7JCnUKARlLfSTOqJVchlciSXxjHHc56Qh+4XmY+PD7z+XLHm+OIo+GbI0d9P1C3w3VDBRojiLVY56jOI85v0+tAv9vR9R2lzFoBVRTArkO82i5NFY2ZfnlG6r/a3Pw7D1fr7QZRMBuMdiPEWGHsB3b9nf5wBZo0Mpm0zJisLZzVCDjw3rE7juwPd5wez6R45d37ASsdv/vtid/+/hcu1wnrDV1nGXaGwyHgnI7pfR9wfsf5EpmXK6kI+27k+4e/4fu3f8d++AbwICv9GNSC0RzTUvjN737i06cnaimY1r4YpY1Vg8crIKI1MO2/9bu27RdSat1sHFazGVY47gJfvR03Qfwe60YtyaMyLTNPpwsxNabpxun5mWXKxKXReXhzHNmNHWmd+PThzMdPJ9al0juv8byjJ9Yzy5JYF8FdHJIszWRMZxWE0xytwXKbyc+NsRs5+h1JLGJWvBdK6bCi17HUIufTC2W94bBIgdwK0hQH2MymtyG6+d8kfENAmt+u1xVlfWWWWllR3J9xehAbY7AiekPwhuO+47DvWNPK56cL52sm1w4jFZGM70BwhM5ifcV5cEFjny2BM42xGZYQyHd76tt71lY5fzrRJsPQ9NHNzdD8BkVPDlcbUqrWJb82DbtKM+nVGqL9VF7UqG5R61lWCatarX9pLKzzBMmSq1CKEDrDrnkoFeuFr447rIXHVJhjYXVCjYbH84l30y88JGGOD+yNw7ueYBNTicRYWEsCMYr/40TXQQiZ0rLamaTS9QZnC5fzlafnZx5Pz8zLSq0e70es7VgWkGbxYtgNI33fsd5uNAopZZ4/rTx9zHTS+H7oef/dnq8eRvaDwwuw6dLUbeOetTbo8eMnfvr0yC83eL4FfpkKz61Rs1e2rEnY4AmuMVohGIuYgpGE95sFCjBNNjC1YFtHR+UuGO4GYa2Fu0OPdF+zxsQ0zeRS9SaVGs1ZrO02GlwkJa0p13Jcw/W68Py8sH5lsE4wxiLGUTezf6vbDexVJxTR9mbvqd7jQ4fve/pxRz+MxPWqWnUtm7baVJPG0baCQml2+97a5gP+Cw7XEBypNR2TX3u7TSR4IS4Tn28fKUWL19aUSEQSCdMyIgGAXCLDrqPrOtZ5YZludL22EPz+9zc+fCgsccYFYRgdh4PVVkwiRKX07LqBlCzLvFKlsN8deNj/wP3uezr3Bud71jzRhUro3PbDtJxebvz4pw9M00zJWafKrZiw2VcPlsXabVFnRLFj2ybwS4igvc6wOsV6a3j/sOOb93fsDg+E4UjdAA/NNF4uC8tq6PyOw/4tXTiS0yNOOr79+gfevb9j3FV++vmZ62UlJ0PNWj/enMF2jvfv32NL5vMvM3Jz3JUjpl0o84TpLdk0lpyJ55XuYugzHGwhmEy1M75UvPSIWD0mW4GaiJeZIj2tBUopmJQZaNjgiFG7k6Bhq3pHTQs0I7Qa9ddfFdt3zTBjqU6z17pJ3Q4uYOwth72jkjg9n3l5WUjJacolZ0Q07y0GhWB4Q/AdgpYTppbJUpFgeXM88t2vvqHbOS7nC3G2lOwwydDHjeRqNAhgm9uGgQq5IU4PTzEaLmgN5V5sdHmcQyThbcZVR0lCjZ0u7jbIzxYox1oYd55+6NjnSpKK7QVK4LauhNpYUiPlSuoSy3Ij5UQqlWo8IexwVrm3VQxrTpSkTaWmVVzwrDkipuBdYOgDzsI8Xzm9PPP49MRtWXSp1gQxnkagZnVqBOvYjyMh9NhlxUqHiKdkA8UT+gHfD+zvlK7VB01FIXXTWRWEM58Xnn565PJ84Q+fzvzv58RV7hBGjAkIiSwrrmrwoRehM0IQg3MNrOD6AJJR8HTd5BllBrSW6HxhPxqeloitBucNKResUzuVVp80TVxSQAJUS06GmYQPRksZi+F0KlzmRn/USLd1+oKoNUFVrFgTB050gnUOcqE4j/UBGzq6QVtn7dUrHAizBYY0Tt1kw2hSdTm63YjgL5QFXhs2W0u0TVsTyYzDSF4tz+fP21hsaFXNTZWssdkMLWWqzTSncJScCus6cZ1O/PzhkfNLRy4e5wtd77h/cOyPgRhX1jkj1eK8kFJRuG1LhGAw1bJOmef2SO/3NGvJJXI8jnjHtlW0fPxw4uOHz6QYt4P1FcpSv0gCIvq101TQl1dP3JfQOpry2P5k0SXHu4cH3r3/jt3xHcZ21JoQq80GT48rNI+IMHR7dvs94gzv797z3bfvSfmZXz78yM+/nFmXipgeMVWrylPElgPvHvYMf1VZ00+cP54JM8qknRsyZdyG8HM5YK1hjYXrOjN4NafXaja9SSucu8Fimyffsi5tNgJQiYllnRlCB0Zbb620L2b72tS7rOEUA60yl8opNa5NKKIHWaXRSsZmodsNHA/qEJjXictlJiVFucFmb2kVg+p8LmzTRDU0UR9xa40sYHvPcRfwEjktL1BmvE9MnVCxPNS2uRgaOF1cNNkWWbnRYgKX1TqlhQVq+xarX7NsJi2baaKFd+IcrglSRnJRpwEmb3YtoZiqRYGtKJ2rOea1sMSISY1FPF21mBnWy0xcJ0qd6YeBZdYivuP9A0vMXM4zzlf6vtCqRs1d59iPO8ZhoObK+XTl5eXGtKQtm29JCebpSowL18vEt1/v8R5CCOrskYB1ga7vCV2Hyb0mkoZAEQHfYTdtvDUFLlkxpJj59PMTpx9PGGMp5si5XrlUy9gMWKfUqqaBjiaFvrP0rdC7pE4i67CdUfyfEV5x/U0aRZQ9IrWyGxxdV2ipUXIl5aqyHQpsb81SS6a1jLgA1mJQHCDVEnxPTJmnl4XTdeXh6HGyBQpao2UoAsbqy8ti9Ca+7Q1e3QPOB3zo6XstekyLgpQaZbN0aawbo8MTTaitYW39yzVXjYcaNeRieAXFYtQ6Y91MjOtmX1Dxfk0LzoK4xjItLPPMbVUBWIzaGq7TRCqW0Fk6Gr6zHI6d9nENnt1+5PayUtdIc4VpXbEYOidIqeTbRO5nhofAkiPnj08c9h27zuNFEW1xNfz44weeTyf1nG4KiT68+uZv9TURUgCBVHDO4PwWvxPUnC/aLFCbATEcdiPfffstD+9/oBuOlJYxpjGviR9/euGXn04Mg2M4HqhmJZsbYpXyfj5/JuYLp/ML8xUwjpIjOUGZC+taGJJQdxl3XNndV85PiSWtdC7g6HFN6G1gpJJsQWpBYiSXSqHX+pOmiwYRCMGyO3bYnJizpS56xdUa4cx0u1DNjFi1JqlEAqboMlPjX0k5qVbITXguwnPKTEZrhpup+vJznmFwjDsByay3CFXN66kpwckY0Viu1K0KxaqBHWWL5pR1qjbKg82SWPOCkciUJooxXIZOq5WXBqmyQ1+qTaoiBWV7Nb7euCgaKCgaGjBuW268Pvtta/0kYVyhoRQlh9OoY9ui3Ub1v1YzlEpXGw+uw/qBw37kOWYuNdO6xA5Herny9PNv2fcdd3d/Qxc8vvP048jh7oiz0PURHxZaLXgJHMaRw7Cnlcbl5cbj44WX88q61ftQVaq7XRfiqktAa8E6S+gGrAuErsN3gd1h5P6hEK8Q+oFuv2OVwjUJZWvdeC3qI868/PiRj7/7STGCuwOHbkcPPC+ZxazYIAQX9OvA0XnHnasc4sIYPKWA63tcD0YstSgnwKCsBjXTVoK17DvDfR85L43FB6ZoiGmDrRvPmjJWKmIz1iR9ARtwYtnvdvjgON8mphh5Ol34/n0gdFaHAaNabdH0LuJ0UafhNHWyNCxGLD509P3A0I903ch0PdNapBWdXGtJNFe3gJTdmLNsdUj/7/nX/z64pVTEKFDXWtUeY0zEVaEq/WBxLlAK5FQotbDbB7pOJ8lMQWalhdM2Cnw1+LDj6NSP6J2jGxxh8IhHo3q2R94EUrqq2RrP++GOQ+9J6cIw7nn39j9gzJ6Pn0/QAm/uv97KCIVaHS/nG3/68Y/My1XtX5vu8qWWobU/Q3brawRWlzNlq+61zWCNxYoK9soZM7y7O/DDd99w9+Zef8AlgRQ+PZ358OnM8+XMkg1TvfLj07/w8+MnrOxJMfLh449aNth6QnA0KVr3kQs0z9AO9MUjqXA9n1jWmZwrtjQtrDMG2b4mYwSrUiniAhkhFaucU7O5IkwFa3F9R5kbpRVdXNWKscoNNbVR1hkV4Cy5Gv3/buHp1rQArjQDrXDOhs8ZrjURTULYPnBUuiAc9pbQF1KK1AjBBhaJ+sJCNj9l3jLkQquWVj2pZuI6sSzLdtAKLvSI86SkXt5cDdUZJoFHAz5lJGVqMQxV2yKc0alKrboN2a71tRVaVpeBsQ1xBWPttvSS7fqZtJPsNcorWScxp9XKzTqk9aS8UteKK6rVH8UQ+oF3BlaTiaaxmsrtOnGunzgNRw79W7wEcnphmh6xdmXcZWpdaLXR9yP7fcc4DLTauF4nPj+deD5PrGujaA3xpmn3jINGPcNgcJ1smE+vRP7N9RKCcLwLzNIQKZg+IENgiplpLYwBHA6ThHReuPz8mTpFbN9jg6VMkbKopatIwxltQTDekoujJ/OuE0Kr9EH5q+NxoOs9rclGltNDnKov4VdFLjjLru/ZhcJUHKGrrBlqE0I3sKaZXCuuau1MSpnBD+z3Rx7eDIgVYo7clhunpzPL8oZD7xFBl9pRucxUxWm2WsDYLQprdTnvtL/N+YDvdnTdiHOenF4n0qa0rqIbCDFGJ2FpNPLrUP7vP1xTUgp7SpnSVOBNubKskd3OMHSBuWqJGjQOhx5MI+dIVhik1loLrGtmXgxiFFZizGuNRMAQaFUY+gM2BDBWiw2XQl8t34xv+dX91+x3nim9YNzIvFp+/4c/cLqe+far7+iHb2lSuC4zrcEvnz/y8fEXChPGb4zSDYmnX5t6UV/rsw15O0jUaaCVu39efnm3/UKc4+v37/j+hx/ohqC4Mqlcbjc+PZ+Y1oW5LqxxZrl+4h9+9y/kNvI33/2aIcx8fPoDSzRYM+A7j3hHzRAnqNEySqCrjnor3HKjLAGnBWLQGrVkqlEiviZI9MDKrfJpjsylkA8d74JTEEcpxNq4xkhbitLXt++v2YY4PYBss0gRMP7Plcmi0dGGeptThqUVfhcLj3WhEvWQbho4cM5wPHQcDo7GzO1yIy89rUHKkVr1xebcFjiohpwNUi3TbeZ2e8FQ6ILfdHB9MEuG9UtVjKW2Si6RJyzJVqJrmAxStazRqFFRXzpVGzCkqvZMq3qtLImaiyZ3jFYnVxMxvmz0eU1kYSzNKfFTgsGYvFVzQ/Nuq1Y39AjBCJlMpDFlg8kdayzMaeH6/In16w/E6ojLL7T8mbjcKKVssdievu/oOv35X5YLn59eeH65KiOgilLfjNK7TqcTtyukqmmq3IraochYhwLfS6axIn7FBq2UiWVHpeO2Vs5T4n4n9LXQmuV2E04XtNonrVziwk/nF17midoFakkYLMYFagu42rHLhbugiyrfKgbHsO+25KTdhhnRyRgVhazIhsmEXefY+cZlLfRWWK2lxIp3juPxToeOWvDeMQyBw3Hk/n5kv9OX7673xNVwu0ZeXmbu9x5nrC7ft+FBZctEqx5Ek1fWWgXvi6NYXZo5v8P3R1y3x8RJXVJG6VwKd5LtWdiGkv9OgOD/83Bdl0Rw5ospt2xCeslFKzOkw1AIXoEQpSRSiqSUKFk3fiJtq98YqERKTHqlMllbOJ3HiON6XcA6dkdLTDPVrFBXjsOBr+/fc+h3zMuV023i0/MzP/70e87TTD963j+84Q8//ZYff/5H5nmhtZGPH25M8crx7Z4dA9UIIp6aMtN14npZyKV+SRJB0Q2yee3M0g4dGrTtEBbTGPuBv/71r/jqm3dAAjTl8vnpxPU2kcqN2/qZ89MHXq6fiUX46t0b/upXD8T1AxVO/QoAACAASURBVB/PESkdphlu02WDTVhCCOQZHAZXPCwdvXmH89AdEtP1BjlScqSaRhFRz7FYas3EnFlTJueEbYVwHEAaTgoSG3apuMVQo1pIis0026gIrgglKWNXzf96+IoxUNFalmqgVK7N8KFmkkRcE0xTL2PZknz7oce5wLJeuV0LJheFCqNIRyUMKSuzVcua4Xp+YZ4nQoC7ux3eu61ORz97y7zinCXXQswrsSRtDG2WW27MVXDNIc1yb5SpW8hIydiii8iyaR3GqtTQynbjMGZbUgASkWoUVQdsfqjtpauf/2bQWKUpW2WXw1qP8VpfFHNESiZvCyZvPbeWmNYTj6cfmdfC+fzIEl/0BlP1dxi8p+ssmMa0LDw9vvBymkh5e6Hw6iHV92zMmVL0ZZVLYl0XSvWILXRdB62Q0rQ170ZKieS1sU49yzJwFcMv58xx5+iPGdAK7rQabDaAY50HjsPI1/szn5eJtS5kKViXcM7jgaEVOtPhXNpwsY7+2GO8NisowuPPZDkVBUTrlEomiOGuM9yWRiyeNRhiVl01eId0Pc5adruBw77HOUPnwJSCE+HQe6bJc5sSjyft9tsPWrRpW9NBqb0CX/X3aLbp1YjKAsa4L/yFfhgI/cDt6ql1RreZjYZTvdbarRSxIU0tWn/R4dqqxsn0B6PAFm8MVgKtqKjlnHZUxbgSUyLFSMoK8805U01hGAPvv77HWc96S1yuM5f5mZwnrIwI98zXZ2K6qu3JNFqb6LwwHo+cz5Ff/vhbHl8+83w5Ma2a4Go2Y9PI7//0G/7hH0+kNFNxGDNQkgJ7fa/VxhhNaLid53DnOMyBj7+8cDllvTY2faNaa3XJ1dpmjt8moabg6O+//47/9D/8HXd3A40FMXC5zLxcIo3Kbf3M8+VHXi4XYrWMuyPffPOWYayczhOldni/ow8Dt883LpdZGaaTwaeBY1FEHFHYD0cyFTtETFK90YqCVBrKQBBrlTiFdnw1Y7jGwuM8Y3rL3gpdgXhtpFUPQyORZAtVjB4mzWPRkEAp2gIqQBP9WFVUe7YCT6XwYgq7fY+rA4lGbplpTnRB2I2BtiWNbjHQ2x3ONIyZEClbi4Nq9OuauVxvpLWw3/e8f/+G/X6glELOK7Vlaq3EVMi5kHJiLWXrzVIrWSq6YFuzkJzn18ayNyo7dK0qKPtfHa6qofKFBqUYiU2XbTrxaRmiLgYRrRlvomb00irVRLBNgxVW24C1YRiNPhtLF4RIw/tK7zqKa/z0+RdyhFpuNFO0icP4bYEaqcVyS4WXlxunlxtavaUttY28HU4WsR1dFyhrxlAZevWpthYpNZJTJaaZZZ1Zl0xc0G6tOVL3hdyEuQU+z8LxXNkPhVFWgsBgO1YiuUJbZ977Hf/5/siHl8bn68ptnml5phsnpMA6T5TQuPPqJOn2jm43gPVqVUKHE7N5xEs1iv9D+ROdM9z1lutSmZvjWiw2VTKL7niAJkYh4laVK6mKFpRgGfuO0PeczldOLyvLmhkHu72IjGq8ZnP8bDXcxugz7qyjWk+2CeeUu9AN3ZYGE+qqf29z823WLN1L1Na2Gpm/2C3gtwiY8gSc8by9e+Crh29wzjNNN3JaietCTJmcEjEmllUni8YGvRCD2IX9fs9Xb3+gtY7T5ZHr5ZF1rjw/Fq2VLoa1azibqSVRreOP8xPz9IHpclObFJVmwFqlyi/rzLLM0BK1FtUUTVaOq92u+M1gxePF46zd4oR7vD/wk/lASnErR9PKj1pkKzV71VT0enp3vON/+Z//M3/7H3/Ae616WNfC0ylSakepicv0gTldaC7gzcj93R01r/zzP//Ey+VMSga36TVGLLkIKesHXpmeM9WdsWYHUbXMzhim1mhJH2pM2JJlqgeJc3R9T8yZ2oTsAxfTCNLwDrpSlSZd7BeuZbSetVb6ahECScFRlGYwTeHPZlv4NbHY3LjEzO8uM5NRRqxOmIoHbM0x9g7nhes88fKSSDHQjwExBWcawQoFrYfJqbKsiRA898c9h7uR42HQCT5lLteZlNIX6UaMJaVCLQbruj9PnLZxa5lzSdRywxnhV9Yyek+RbVrdvjfnAk3cl2gzvC6zKq+tEkZkcz4ApmJsVh+sqVttDEBVupb24VByhK2BVH2dBWyk9Y026HJsbZk8F1q2UA3iLL239IPHii4Dp8lwmxMvp+1grR6a3W5Xrwu4gGHAu0IIM2/e7PjhPxzp+oizmc51UCt2i3DG2RInR5wracmAw1gPbkcyhsdp5v2UGMfK3cFz+/qOz6lSl4WuTXQ1cfSO7x8818MD5xxZTKZYYU4JJ8K9TNy5HVaEsB+Qbtj2E6iRX3/SeqXe2AWIxXm1eQafCFatfiIBK7KlHLcbJdCUaICToC0big7BWcc49JyvUYe264WHu3vEeC09rcpXaFX3BqZZwH7RXtWCabDe4rwmt3wIChsSu8lIVeE/JmGqulwQaOK3xd5fcLiWTRcppVJyxVro/YBBuE1npulC3NIstVQ9/ErZGhn1DtNqIaXCskyYdmOdBpw3jMOBofdcLyuX62es14oVK1qcl+vAsmZivun1qQitBsQ1xMxbWkpbZXl9m7BVtJiC9YEatcO9ZTadpGhu3oKzHeC4v3/L4djjg7ahrksiLoZpSpyfz7QEIpoN/9u//Vv+/n/9HzkeHMbMVAzP55XbopUUy3pjjhckGLztCH6PF/j06WeeT7+wpsYQduD1sKylkFKj4RnHgeAbNjbEKttTX2y6tMjrrD7aptawUlXbds7Rdx2971jiypQbt2aIKVFMxjanhYNW22XFJZqJVPFkDMU2vEU9sEk7stwmB1SjwgcCi8n8Pmb+WCrRskFRdJoyKB9zGAK5Zm6XlWkykO1mcwLjNJxQm1CTTqN9t+Nw7HGu4UPFiAKkc6nEtRKj5sNra6S8UmLVG5RRy5Y4T+88A5YlJD6dr/zjcsbaPd+6npAzPmkqKNvN/VErUpsuutimG3SK1QMMlQP0lIRWMbVS2Nii22JXTXlVk19SKEE1WsGwSuHSZc7SuMrMUiIl6vehraJCQM8fa9XmWLPnuqxcr5F1AZrfGB76MhSrCzmVNhqHu45vv37DN2/f8PDWM8ePrDFh8dRWqERcKIgYllW43JqCcbzV5JkTGoEpFk7XyJsA41h481cHShFePj6T5wWpivk8usBXXYAwUr1O5edlJa8dQy0EBNt7hsM9YgOGjS8hFbFla21uG/10awowBqQRQmPoDH4GVwxONAiQa91uZ2Zjtiq3gi3z37ZQ0Og8wXpuc+bplPj+a6t9ZEbtpLXpcy81Y9rrAnara7JG/xHBeofvekURDjvW5UZNW+S9se03tmBC1cOdv9iKlRfM1vHeSsEbaLWwxlXN1abgvI7LORdUPLZ/Nt9uCai0Zp6fzjzVjDUzIew4HAfE6VTSjZbxoFUXuTQslt3ujpwLMi+s6wJh+1AbraywxmqNslHMWjMQrNZd59yo0XA7R24n7WyvG63+9R8Vpy3eW1q95+GtRnOd9PhQcL0DE5leJrwxvH//lr//+/+JX/+H91i5gDSuU+Hz8wqm1677EvHB4qujJeHt/R4n6kPUKhFLpmElEVMkRQVpDIcdh/2O5XTD+j2m3KttRBpSKzVHqHmTK3TRGMuiHzQxpKQErmYaTYQlVZZYqEtk6Br9qG28uMzgK+IaftvkOhFSWwllweWAAZqJRFsxWJKFhcScIv9XhM+mcU0ra56prdH3PSHI1p5pWFLmNlVy3rb21mCDemxNgpYrMSXWpWCNJcUbKRdSEWoLlHzldoaUKtYKNjgMeiuible0og2hXfDsuxEQ/K5jGR0fzmfWdObWhO+6jn4tDLkhWW1lTV7TZEo2c81o6ux1ks3bf7NWr5OmfpFHxG62tPrqe2460UhW/b4JyVjOvvCzROZcWFnJuW5s1UXbMTZ2gLi8ORoUQn49F9YVatGYrHMejOblqxGaiZvEF/HW8NXdwNvDwOAra044a9WnmTNrnCi5khNM88z1OvFwHPFhwJqGNYXUIrcMn2+e+13F7jPjg+Ov3Fvu3+w5P05cXibWZSa1BZMXdRYUg2uNg3HkYWR0hi5A99AzPvQaEdfRXj+zTmi5aSTbNDLooqqoxCWm0feOMQhhRRuWlfgEGGrWJaQYjXLX9rqI1je/Dx3Be6Ylcb5W5dvukra+Fj2/Ss1Ii0gTnTZN2DzNgvUWyRaxFuc9oR/phj3ueiblRGsRQ8WJ0eYPs5VmNvOl2PTffbiWHGnVQm2Ypm+RXBIpN7zrsa4Q06Qlf62RcyFn2cS6tlGlHLXAugrX65VWZqx4rtMdh7uR0Glx3u6wY74tvJyu7PcDIXSbTUrJ5NpMWqg1Yy3I/83ZmzRZkp1nes+Z3f1OMWRW1gAUQIIkSDRJtCgzmWnRC5k2+t2tjcy6TaIkii2AJIBCoSrniLiTD2fU4vNIQEvWoqwWWVGRccP9nG943+e1GW8sXdez6QZudwdu9vd8/DjzT//Pv/Lw7oE4NigOqqa2tcVcW9jS1MplzLz+/i2XS8dnL+8ZhoFWZ6wp3N3v6a1FFcvPf/5n/PLvf0bwGW0qscCHxysxF8GhoQmhw/sO5pm7w4YvXu2wxjFNPeNS8GZA1UprM7UktNLc3mzp+o5lSeRa5TatcrOLLjChSsKuTAOaJmXB7tl1Nlwkj5lhs4UE8TxDtcSS+BhndNbcdR68wION1iSSnBMtkKgsBjwVnRaKWpgHzeQ0qRaYI08q8J3WjG0k1kwsbdUHQ6tWDnetWVJlmiupKIyT/C4TNLp6VEmUVklLJqfGrBYKDW0a2liOx8J0zeS5YZ3GO0MKBa0buUoXJTASQdDtth3brhNRf8t0fU/qDel64b2Vl2EbNdup0FVEWoeGKk73qhRFKZ6XEloh7jFAl1WPq9X6xxJiqfSzekIWS4oGNUucc7VcdeXJFo6uUkjEpZCzohZZlOrQcF4T+oqxmVI8abJcToW4gGqebTdwOBzYbLbQNOfrmSkeUbahXKLWBU2FesWqLSEEbBKovDaemBdyLiyz5v27Kx/ej9Tc8H6g6Y5LMpAq3huaHriWwGmcuQkjfZdxdw6/37J9tWO8Nq6XK8v8KJeaEpyktw7T7dDW0XtQNtGGht1ZadnX0Ys2RkYo6yjgWcGxSoVgBdp3VtPbhiVKTLq2lCKLx9yqyDmR5amcK2IoAHDGMATHeE0cHx84nbfshh1aiTEiI0VhqQnTDLWGlfGLdCzmmfUqkizvO0K3wdqOpV0pRQhpILWj1las8/X/jyz99x2uRYb8QqWX2emyLGh1Yegd2snmrZRKLJqcHMskt41xzy2NIbUkOr2iKDkzx4lxTKS048VnLyStVBmc7ZjHmdYU0zRRShImwAqoblWhVYd3mv1uy+d3n/Hzn/6Mr7/+EXf39xiz53/9z/+N/+N/+z35alGlfbKsPXuDJElA5oqNdR5XFNN54V3+yIuXFdeJcNgYy3a/5bC95xd/91fcf7ahqRkwnM4zDw+TSHValSWQssQoc5l9r/F2pNSR1greDaA2LNNVdHZGIie8le39+ZpoGIxvtJqEcIWVKJq2SkGUphQlnNGyzgdbQ9kOHwacgzk+onKkNUUqjVPN1KwoOaAOA7Yq+pzIamYpFW0SxsLFFarN2C4RVWTaaB5d5lIWrLfUEPDbwOGxh2Mj17TCtEUn6nygacW0RKY5orCgGyYYiWOPBoz4sGvTq0732YHTWJbKPCdaFjBQLYqlQiFifRXCVVNUBS5oXK/xpuFNRrvGgIx/lO8oe49e5VDZWEZT2CRNj8Y3jWkVh0I3AZSgGkXmAdAkK0pl0RQbLTIzSfx9fhnWF1PXdaaYMCiaMmQH80Zjbww1J2LU5CgzROdX9YzRWONotXE+w3Ru5GpkK95teXn/ipcvX7AZtmjlOF7OvH77DWP8iHGailRgXVBsdoHtbstp2UERcv84XShZcTkVnh5nKJrOCW0rl8qcGi7KXNYZSby9ToqlN2yCoTgNthIC9PeeQ7kllxuRtCktPNzWsMbSSqbVWWDiQUt2W5NllVxCaj0Q66fdjUKKNbR8/lo1goHOF7RKoivXmlQKpYFeaf+lJIqS/6cx9lPhZW1g2wdO1jBNM+dLJL9cg2a0WKwzdXVoyh7hmeTGumjTRpbZxlqc7/DdBhcGmnoSnbmWkNZahN4mz3FbnSo/4HBtz7561TBW9F45J2KcMDqhcqVWTS2Glj0pGi5PV1KdefnFLbt9T6mR08cHPj5MYn+slZYbkDjZGWPOlApxrixzpLVnC58QxQ/7Hbd3B/rQMU+FZaxstls+f3nHT794xV//9MfcHPY0PK/fLXz/7UemS0bjqCwYXdcPAjmkW/s0Q9FKYqp1W1Fnc+XxwxObvSdsDJJhWYlt5A9vf8M33+/46VcvUdXx+u0T7z9cGYbArI8s85Upj4JVNFDyiZIKp8uZcZwpuWe+jszjSNc3fIDWIufzmevUc54aN4PDdaCyQiWHVYpIoihNBDKKTKE08bsbq7BuS+gOGKPJ8Ym8nKFFGpamJShuap53kybrhtca1xqzbTy1iAoF1yuOPpJ0wehCUpFoICpNRjHrRgsjwcy87BXb/cDpqXI6RpZZZurGygIzrbNvaxraIVlRxrB+0NIelz8Ky+WMriwxU3KT+bCqQlQzCusyNhSJ+LCO4Hr67YDrHNZYlEo4Jd/fBIdultwqS0pMJXJ1CT0UzrNmSIoNil5ZQlW4VtDIxZhLXbf+kNdxgEKvuLyGqYam4qfl0jpXQLgNMoBLJjPfecLnA8MmsXycmEZJaTW2EbTCWo01npLhOkau50wpCq011hg659j3Pft+y263p+t23N+9YrvZ8s13v2ZJT1QmmsoUldBesRk2WBOoTuaaMYrjT7WAtwPVZXZby3bbobUlZ/m8vXU43Si68FQ176NjyIrBGGHy6gRqxPgemqVKfU6tFVWzuJiUZKNhHLVpKKIZV9p+Wv7pVZbRalsXxM8wdgVkUV+oQnBV9g5aocnQZGyitXSeMU5YtPysDXKtmCpjQu/FkDTHhafzIt21V5+ARBQj48ECRVW0zZ+MOErr9eddfw/W4XyHdR1a+7X7TuQV+lRXtUkFtPmBC61WyvoNwXkvMN2cUUSUyuLUaB26BOLcOJ0TS7NMceLz0HN48YqPD2/JWdOHAzk1xvHIMkesc6jaEWcZONesAAGvQENpRQiOm7sNX/3oBS9uPyPPHU8f5SXc9z33Ny/ZbjZIhrjjw9v3vPnD99S0YJD28VlO1dZY7FrXk5V15qZESqMBXTVlUVzPM9oH+s5SWuSaHvi/f/W/c778gf/pP/0nttuXfPf2HQ/HC5UOxZW3H96gfEZbGaKnODKOhePTmRgrpSbmlLhOIhc53Ihc5PQoFHmcpus1Xe+wi8URaDkTayPSSAqSUiQqmYxqDY/kjNk0Ml0nUrnIfEivszxRupO1F0VuvbBRFhUss7ccfWLeLjSTWWqjqIJxoIwcMLoZlBLsYa1SwRnT2GwUXae5ve95fJg5HmfCWqFWBb4TWZcyK8NgTdMstVDy+oI1caWVmsSavGprla1YLwsHaxu+M4Sg6QbovGPT7em6DuUVxho6VfFrZ59rJU4zNYuleY6JJYlEsC0NlxS3buDWBobWCFWcb6w1hG9KTAertlFoUTL7ls2yQ6mKVmWVcK0rVGWoSrMEmHaGtl2jSVwPaibntHI6jChwiuEyJsZLolb96WKiaZzVBG/pgmfbS3trbOD2cCB4x+++/RWX+J6iR5Y6sixn4rIjF6n4clyY55lGox8GXn1myDGx2Xo22x6lHaU0UmnE0mTBpytzLlyWypwMG5MEkL/6/LVCWBZVYuOF7l+lK7SWlqVFVivgpyLvFMqImmVdbtVWPulMJZJoVXPohlaJrU4cTONRKdCZpvI615bKVdOh0dRSSTXhVlVBbULqc85zHkceHi5cronOS4ijUSsUKTeaqVTdqGvFrNa/q1S4q7nAOXzo8KHDOU9UeuVLyBIz5yzFmrEY3f2ww1VRcdrQ9YGu8xhtcCEIliwtIm6ulvO58P7DzMN5Zo4TELmeLiz7gfk0orNCl0acrqQa2Ww6jAmU3DBKtt0Exew0MUr8hKoS3Y2KjOMTV9thmluBIgpve/bbDT4EWrHME3z7u9c8fnig5oSEjalVKiEPb2mFurYDZq1ajVJY/Ty9kSH8c+S2NkUgNM1ynSd+/ZsnSoM/++nf8PrtAzEVNpuv8LYwTR+J88hSJUPMac9lgqeL6DKbSiwxkbPMoMBjjVsxjplWKkZ7jFOydLGFeV64xJlLjSxUoi4UV1DBSBtqDEtL5PTEskwo1TDKMRiL0oGqDLFEiq6YTcPcGR6GxqIiobfkreVizuS40ouMFiqXUqt2+nkeKQtBAWZIdIs2Ct9ZfDewO1jJJdNgisK4QI4yI4250HKUSBZEK6xVlSDDmoh5kfGMNoTesN93eNcxzwvWVvb7Dfu943AjgHKrhGGqdJTqpkprlnJjXMSBFuck0sBcmZeF6xjJsdEbj+6gmIxLiU5XghV7t2pgU5NWslV5/mgoJQyBtlZQyhjQFdPq6l+X5IhsFPMAY6i0ttBqw3tLFww1RZyX1FHQXK+R8boIfN6KVVU1/YkUZqzGOoMPjq4LeBfY6o7w5z9Dq4lff3tkriNWNUqaOF+fSDmy63aUmpmXC5WFLgzc3jpabvQbt0a5SAusVmh8KnKzKDSn5HhaGlsvy8iKojSLaw6jHXrNXZPQanFgaa3RdVnHd+VTV/jcPovo4hntqdbDVS4rkcQ9Gww0IQT6rqJ1IutK1kI8s2tirapyiLdVhpVrEXhOK8J49R6a4XKOXK6Z2334FPuuWqZVuUjX3+zKkmDVsj/bYeXzd05cc9Y6lLJyNDQxN7RmsC5gQs9md/hhh2vvrfyCe/kFG91BsZS40BbNPEYeThOPY+G0iAbRY/G2wTwxX86S/bQ0eu3Z3d6T6garMs45kpa/tLUSyxwQmk+thRgz2iSmUTF3hrqN0jZ4g9EdtzcD/eBWVYLj8eHMb3/7e56eTpRaxFnVBCtXm9QihUqqGVjlHuhPh6xWz6W+3MjLnOiyQxslbbhW5AbffPcHlmw4Xo9QHF+8vGG4a4QQuRyPLKUI3NdpyghzMrJEy4qW2kqayni/Y7dxTOcHrpeRVBXa3GKCIdtIaoaFhTHPZN/IpbGUBR0Q2nrTJOCpjvLA2kpvPAHHxjqM2lF6T+4v0I+YAWpfGEvE2Z6buwO1fMDOIu+qCjQa0goWbquTaQW5iKdaKtC6IgbFtAChX+lZLaNKkiwjI9i3UhMlRknCFRYztcjtn2smlwKqErxis+3Y7j3eOfYHz34fuN1vCX3C+yTtZZUDr7ZGjok0Z+alMsXGko0s09Iiz8Vqr11yBa3xt1v8zcCSZ8YxMlaNWX3jWik6relh5QVUDOKYk+5n5Tog9C4BEWmKhtk0ymAYQ+WaJ+yk8F4cPZudxzupwlGVeSrMUySXLNVh1VDs+n2sFATrCy+0sYJyDWMNe9/zZ19/SUxveftRCG/WecY4UXIkOEdKE3M6UmrB2Q3boWMaF5TWDJsNDU0pUdrbCuO8EJylac85w8cpcj8oQhbKm9JWqjT0n7hYNWgLVbblWq1MBvUctWJoTf6t1s3+s+RNukfJtlKqiQa+WbFiK/B9xbmGnp73JFCacDMKilwSVRsyYkTo1vmuVuCdvNPjmDlfZlIZcLYhWBDRzdb1eW5K3vVnyZhcbBZjDcZKLJENARMGcIE8F0ppWCXZZt32wGZ/x/72/gcerl0nEIimVmtcJF6uqGXBxcz1NPNwSUxVYnm3RrPrAsMmYHylkOm2W1Qd6JqjM4F4PlGuR9m4h0ZSBVxE+V7kOlSRUxUZYB+fTgzBsv/zjm0fcNZilef2tqMLQisqxfD779/xL7/9PafxQsoRkKEAz1rRVsn1GZorMjFjJbZXYpuL3KhYVLPo6rndviC1J67TWTzGzrPkzHfvfk+pGVu3PDx+z/3djsPWUdKOD+eJOUfGKUJVlGm1AVVxONVaiEslR8vNVzvSNDKPijFqfB+wg6IOheXpylwjUUVc3+GtZdYC6CilkXMlk4hatKZeW7z7Y/uyvzf4O0/qep7mM9dlpDZD3w3cvPiMbueJlxM2G0pNa2aUkplZfaYLtlWxIUsrtYZUlipjG5ocOEqDqkKylxDL1bpqG5WZtGTSshAXQ0mWXBqliHKDpnFeDqHNzjAMit0mcDhs6TuNtQWlZmTnKxrOmLKAxMeFnGCaK5drZsmGWkXLaZxs99vaufRDIAyG6hPNFQqGGCtxXihKDq/QFDtlcQrhLdTy6XDVTbCGqsp8VSlNoVB8ozhD3iiuVpGiIlwKrRPmQvCGznlKbVzOV6Y5ii3caFKWn78ZmfthBXYiB65AUJ6dTUpXKInDrucXP/8bXrx7wfFyxnvHx+OHVdqluF5OpHbB+y3ODZRZAzMxRbpW6IMn50bJkfdPj8zzzJeff4H1A7FpzjFzjYW9k1EAGqrKlOftvH7e2K/YzlZWzJ8iN4VSkpu2Pgmr67R+KghaE8mmjJnWzkgZlGkoVei9ZbAKXxNOyYgmp0jRmmg1Go1uhVrtpzb92cbsrcY6yxxHzpeRlG9pVqNUEf1yWS+sWsQMoKo8x+szixZLrF6rWO89LnSgHU05tNH4ENhst9zcvmR3+4Ltfv/DDldfBnLVXPLM6XxkucrcatMaJsOQLMYYkhIe49AaB62xprFoxbJEUmlY5VAV0mVBnxN+UqhU0KZhXhZU15Er1KrYKsPeQdWKs4JLynTOcnPTMfRWBNWFNalA06riNEd+9Zvf8f2H91zzLFlQckyjxKMmE7QigmB5+fUnhwYg8g7dsNqCdnz91V/wv/zP/yMfnn7F9yVV1gAAIABJREFUf/3H/8JpvMgg32hymWSBUUbevv+GVy+/5OXtCw7DS/J3r7k+fmBJWZIxY6MaUVA01YgpkosiJ0PXbbl5ceU6anzybA8droPFZZZaGONEbpHgAreHLXd+g7KKN28+MI1RZlhK2q6oRB+M0fhtpftak8yJp/PCeRKjR+h77l98xuZ2S2Ik3Axs1Ib4dJSI6ZbXwDXx/tcit/Wf+rHRGqPlpS8ly5Z1jcpJCRkVIZxQrYxwZUuVKjYLRCbXTGlNdMbestkYtls47D13tz3brafvJc1VqyRR4NmwTIVxnLhMC8uSKRHAs0SYF/l8u86swHQhXhUD2lZ2e0c3VLSrlFRINTOXTNZQnQIK1ThA5q7KAFH0saBwSoscTnpemb2phA+OcPDMvjCTMFWjS0AXT86ZlBaRKSaYpkYpFmMdrRVykXZcV+GOVifLPqoRljGSV6eQy0uVhFOw77d0X3RMy8QYFz4c3xO6AKqR64RWhZc3t3TmM+LlwkVfiXkmLVe2wRN6z+m08PTwQM2VfDtTy5alaK4Zzgle4rGt0coiWhulUbpHrVZSYaI+v19B0IJr0VKqFDTaCtZSAPTPzI41TkkQLp+29aqBVTBY2PlK0HBVmlwyeZm5Hice0dwcbri5OeCCR7dGKYVi9JroqnHBcJkSj8cL1zExWI9uUjXXZ0NIFUwnxq6qoTVOfD1crbVYa3FOkkGc99Ra6fqBw80Nh9s7Dodbtrs9/dD/sMO1vK9cs+a4wDmLd/6gAxvdcVCaztTnWTOKiqcQdKXlwnWBh2VhnmYqlqUpVKz0qdIphVeeVCvnHMkx4LqAUZmNtfzYa/apMZWe98WQHgqv/+13dLdHgr/nfv85oTNru2b48PHI7775lut0FUiy+dPNpBymMjxXz2Ksddso8gq1/qqfq67tfs8v/+7v+Idf/geO1x2Px4/8+pvfsKQo1VxZl3At8/HxwuvXgRfDPZ2xhM5je0NdGjVqmpGbn1ZEcWE1pVRJe0XR9T3DPmGiFxAKimEzEG41l+OMrqLp3d9s6W86jucL1im0gVbqJ6hMbQmqwMr7YDnWM5frwngN1GJw3nF/e8fhsCW3Ca0zt7s9ZTnyWI7klteLx2DwSMdWqCswmE/ViLiXtGkCuugkxrxWuF4WzqcoS6v63B42jJV8p1Iq1ie0X6O4lWfoO25uHS8/93z22YbdJqwLuUUkecvCeIkso2G8ROYlEkulVYm6KRlybhhr2RwU3slsUWuLd+t2Tll8F1F6oZRKLvoTm1eBXCIItrEqmSunnIgpURNYZfEWvJHW03qDco1u06MGx+QzEzPaGvrBs9sLeHmcIkt8YJmTaCWNxVoP2rIsizjsdFtl4YVaNbmI+qIUsV22UlC1SeutsjgTm5ME07XDq7nQOQG2pyyml1e3B7Z+y9O7C+PiUElkYyXN9GGLNzJnDtai1njxtsBiDKcFrknjdAK1iF12dbDp9d1RzxULz/thebPaM7cZKOoZti8SKEmqkP+2sRY2Sskcs8olE0xm6AregU6aWhSXy8S79+/IpXFzG/mqGm52mq4rGK3wzojzykAX5G9yOl05Ha/cbQLarFplJWMBlRNVW5q2NCOpAs8Ywmdex/NowHnPMAxsNhv2N7ccbuRQHTZb+qETSM4POVzDx4WYI5tWsJ0iDxqnNbpm+go3GVwVLaAPEpNslQiHF2O4q55rbyF4soEYZ0zNdE0T8Cwlobny8ZrJdcT7gPUeGwubarjBc2ccp1Ni/OcTl/2Z/ReZm7BjGS8MxpJL4Zvf/YH3b95j2upfb88koyYSEKTiUKwV6jqHTavGslMap6Ghsdrx06+/5h/+4y849IFteMX/8Pf/PcfLWcAbS0W1KqYE5UnF8vrthVeHCzf3B4ouKKcILoDv6FujkkhLIqcoovqKtPbpGakn1cByGbmUSjAd3V2Pe+8IRjLrHx7fUM+V2lbegLPidKlZmLKUddGg0X6HMRnvKnY7sCyFzabn9rCj5BFlIrv9luAVtVpKNdRWVpjGH1s6pcwnj3pDOgu0BA7uboIE5+nKPM6cT5LKG5cq1RfrLEzLJee7gPGari94FzgfF4wxvHp1w5df7bh9oURYr6Bk0UVfroXj05XruVKypWapdnN1VAytauISKSVz2Fp2ByfmFh1wPqG1VEZiBsqkHCkZ4UDQJAq61TVPy9OUIdbGOE9cxolWFdYa4ZR2Grcx6N5JZePkYLjWhdIKxju2ux2Hw0A3WGq1pJPoueOSsVbjvMVo+bOlpdVSWaFlyaFTjbmMXOKJpYzEMrFki61g3KpeaGC0xVmFqYF0OrEsC0PnyXkmtYTzlsNhz6E/cHd74fE6UnBQDaXJln7oOj5/8ZJSMqXBEjOmwWQUp1lxHCsbpwnerPPZilYChWftloTN29YCZpXZNXHPtVbINeOVwWjh7aLcmuawMrP1cwdp1nFdwZhE5w29D9hZLOsxG3KV32VtmnnMnOqVeTKyFFRIuq2Wy89ZwzRNXKaRom7xq8xKfg5JCm52Bf+3uiqixFyj1w7NaGG9hhA43NzSdR37mxs22z1dP9D1PaH3OPcDD9dDVAxVkRUsVZNwuNAx1MidLtz0DtOU5P9oCaUzWpZFt2HHnd4wY7BDT7WNWCeqEm2c1o44VuxoMXPkOC7UMZEdnALshy2ezNZktl5xjY3TxwVV33DV8G45o776GSkVfv9v3xDPVwajsThKEU+zZDHJnFUrYaDKxlKqvVJlKaCUzL2C8uw2N/zyb/8DP/7yBZqCdZ6vXn7FF3df8vb9O5Y8SlCgsbQSSAUezpF/+f4tX3vFmGZQDd/12G6g5EiKIjUTq5ws23KCeVnwXngMnbM0CvMyS3/UJAbaBg80lulCbJPg8Jp4qT2WrCO5LFRdsVazHfZ88epH3LycmeZHNDueni4sqTCNZ0Kv2PaBvusxGvrhlt1uYZomWk4S17MuG2pR1CLEdoGNK7bDhvsXe4atRpvMOJ45nk88fBw5PWVKMogr16JVRBsZKVjTBGTdGTb9LV997thsO+5fdPSbBvpCaQvjDNdT4ukxcj7PKxkqYLUVEXyulFpJWQDO83JF6UwYNoTOIwy0hZgvYoJBIDkyKmrkpEnpUy6FzGWbwRgHRKYYmUrGDj3boWe/DWyHDhcUjUhtwrCNNZOTOONC37PdDex2PaFTpBh5eDzz8PFCigKMcU4RgsfowDyL0iDYVS2iM81ARPE4Z8zJcDPt8IsmjTOPSwUWgtHc7e/ZdmsUkTY8HB8oJaF0EzSkKgRr6Pqevu+5vTswfHwklSrixFWWZJRit92y5MSSC7oUrLOkopiz4Wku3GwU1jisAtPEidZWC7asLmRu+pyAKt2cyKt0K7RqqFqzRLhOAicqKqFUo3eBrlsLWflq+Tpt6L1n4+oqp7QMmz0vdSbHyHazwVtRGi0189QuxCWxP3QM214+a2u5jBeeLhfmNNPZXpZjauXwtufNauU5Jvv5QHXWkoxZDQWG0AWcd2w2Ozab7Rpk2GODx3qJiPlBh+vWOnLMtJQoupEX8EEAt7dbx023/ZTymK8TbUl4o/HWYtxACHt0kQWAbaCsQwdNdZnSLFlbHApfCi+1xrhArLOQcoYgBPL5gm2ZV7rjhe64XCLXf/2Wd28+cPz+iWvr+O73/wT5TDACpyiqiTFBNZn1rbOg1ee1tjjy2jUUsRVaUThnefXF5/zNL/6S7cahVKSUwrRU7u6/5LD/jst4JUeBhlhrxStt4P11wrx/T25XyVVTFR9k8ZOb6OmsVuvizJKSAGsOB8vQb7i/f8m8XLiczuhiqFnje4sqEmfdsFAcujW0bjhn8EOHMlD0gvYZ5xVDGKicOJ2P5DTjnfBeP3z4yOl85Edff47zN9SqmJdFYDL9LadjYh4jfd/JIgOxHueciCUzLULkv3sZ1odYZHJKCQegFCO+eYccJD7IZWuqzA2VzHCt6dhv79hse3xXMC7SWuY6Rk6nmYcPkeu5kqKh5l50w6tQuxaRWC1JINB9r7i9HwidYthCq5lpOZPrlVKv0PTqvLGAjCVyWlGYBdrKsdXK020t2mRC33F/s0EZh/cKZ8UQ0UjkPMm2uchixnUBYyxdN9B1PcY0pkl0lo+PCaXCmlwMIYjUKMZKjJOwC3QRprFrJAo4IWQ9TY/82/f/wtvTd7RWiXmBEuldxy9+9gt+9tOv2Ww2PJ5Hvn39e1qT+Oi0zrN7v8Fqj9GK2/3Azb5jSSdiXmEqK+mplEJDHIYlJaozlOJYmudaK9dUGVyV+WTL6yHkZJbegCaup+ethjJaFn6lYJpmTIE3Twvfvnng3fszMSoqhWHr+PGXe37y5Y79YFFVUWpBYaFVOgv7LtHpxLlquk6z3+xpsVBrEEqVAq0Ny1LJaWJOE4dc2W1lRppPistJkIstrAtJvab6NiN69/p8wK7qmJU8ZozBOINznmHYYKxlGLZ0/SDa1y5gvcM4JwD+H3K4drdb6rxgZoFTLABLxHeK4WbLbn8gdDuJrjiOzB8+0sqMtgrdWcx2IMbKck3UUumsYdt5lC9MEUYSXV04lBlVGt5A2G053H8mpf35HdUjoba64ZXCt8BQGpenyNPjN3xMC+b8QGgLiULTIlpuSNSHXgfojWf7q8hFmhZbXYZVqmWwXcfP//av+PFPXqF1QpnGeC28fxgppef+9id8fDhzSUdKrnifscFSmlSh797NOJexoae0RDUJo1bJF4rN4Bn7yDkXcj0zToWhvyf4nv3tFjs2rpezBAZqw+62ZymZco3CYDKKVgpWVQbv2W4C3d5DH9AhocxESUeejleW40xNWqJAsmGaFq7jyG5/4LNXliVGHo9PXC6TmB2Oib7b0XV7ILEsT6R8ZUmZy6iZZs3+5pZu6AVkbcxKbXKkBeaxoQj0wYgG1jt8kGBDZwJ95+k6Rxc2BBdQOlHVwjRLRtTDh5HLuRIXC1UD5ZPtdF4mqWCZaCS6jeP+xYbtVmOdMBhKkaDF1qxsh5XoR7UW9UepEGNewyglPy3OmukK/VAYNmLNTIsV+hZpnYEKHY6WpHWkE2AHkVwiSle839B1hiVeeHh84nIuaNXjQ48PmhAUPmicc8zzlWlccM5jvMEFg/bPIGwh6GeVeXt6z8eLQFu0lnDK67Tw3377a47TA13wPDxdef36NZ+/GGgNcs20prF6A0jk0XYI3N10HC+nT/IuWTAJ+1ehqbmy1AXrFcOmI/Q7CjMxL5KztR6gtTbUM5tVXqBViL8uPatE/Wg0l9nxf/72xH/91R94+1GCOG0LgCGEyvffv2X+m5m/+Ytbtl2Q/UmV8Z1XExuX2VjFyTRqi2ik252rIheBez/PcYvWpGsFNaJUB0r09+M1M06ZOES8flYmrNUrRUA4rJAZ9SdO1lXT7Lxjq/f4EAhdjw8d1nuMc1jv0EaKhh90uLpXe/qmcePEPB4xLWEHR9hqrOuoLoATMLUiM2y3pCghgDZ0KGUx6YyrIwqN1ZbOe5pJLCqh1ELvGr7zlHGm1YWtOXDf7UlLJCdDKQbrM7oTpFttFlsNmymjpolWCkfT8wTkKoL0pBqFQmrlT6pWQcxZrXDGyMPYYF6ti8Z0/PgnP+Lv/+EXDIMCnUgl8/bDmXcfZ06nxn7zBX/1s8Z3b37Dx4cPpFQI1rPbDNCagIqrg2TJJXM9P8hhaIU9Sm3sdwGlosTAzAJY6TcD1mlccGy2G7IulDmRSiP0gfvtnk32XC8X4nWmLiJ7MqbS7yK1f8/SJlJMNDTNWsazIy8Z2oLWPa1pYiy8fnNk2L1n2MLx6ZFprjw+RYbNLbe3N7RamOckwYFWob3H+D1Dsdzc7Rk24nppSuRFuSZSMrQSUMqQomyHx+uE0hnnLbcHuxoBBvrQU3JhnK48PD3w/v2Rp8fIMmtaDSgcWme6XuN8j3Weh6cPZEaJWR8s9y8C/aaS4oVcDHlFSoq+Wa3pp3Jo5KKptQoLtglZiWaIS2G6wDxVfChsto7DzZ5l1nx8d2VeFoQf6rDaUYum1kaaG6VE0DNhgJvbLdutZZyOfPz4yDw3jNkQugHnLaEzhE5CL2tpLIuAj3zQMpO2iq4LGBOQRc9CYZJqFCVcUg3YijWOMY188/1vSLmyzBqvxD+fShXNcHWoGlBVqkBjGrt9x9B1LLNU7LkUsXuXtv4Om+xDvCYs4iI0Fo4GbgeDd3Ic1bYWKM/RLWIAXbkBzw2+Zs6af/7DxH/+v77n+6eFWjucUSuIPBCzZnp/Qakn+sHylz/xBAWQaYjJIDhN5xTaRFop5KoxGAkULSLlks2+R60JDNOcMOcL1vYYY1hi4XS+cLsFZRWyXZa0B9Hb6hWb6VBGJHtKiWFBK7WqBTpcCH90bPkg74WIgD8tzf/dh2vzFmc6vDOYQbMzBhc8TWesqrTqWKZKOi24NDI4hQkBZRxVWebxQpkvqGXCKI3Ogdaet/gFVBGSuwu0oCm6Un1HseLn3dQ70mWklVnicKwmo8WqWRZanTG9JwZDqmCSwaiCpF+YFZ4t0BO9zl29NXhtkIvS4IHaLJv9Db/8j7/gZ3/+BUrNVJW5TJGPTyPjXGgVtvueV1/+Ga++2vGrf/lX3rx+t4K7DV3XSxJuE/nS9SkRY2PoHH6nKOVMipGGZthaurpDAfOS6AaZV+UsFkbfd2jTKDkzThdSy2x2Gw5fvKJEyFNkmRbCzqD6K1lfRa9Z1kWUKTgvWslSAaUoawbW5ZL55ptvORwcKUWuYyEmT985jsczVEXfB1wo5Drhww5rB0rTHA49zj6PVZ6JXAqUx1iJnbEGYkzUqnC+Z7878PmrF7z4zOF9JcfI09OJN2/f8e79iWmstNbRmqGh6Xzg7v6O/a1EB6VywW0apUoL5qxBq0UiTIrMTD+lZaCpdUVONqCtqMlcibEg3WSGUrieE/PVoY0HRPPogti8G4rjEVJM1BZZYiXHQoorakMn+k5zuO3xneZyOfH4eCYni3cbQeAFqUq73uC8whjNlCLXywJN4byjMK9UJbn6tRYgiVUe9GpuaEo0llYJXcwIiem5GderyygVScwtWQJBC8J01bZw2O25PWQul5Ms9YrEjk9zFMNLen5PLNdpopYCh4GdrUxJ03cOrWZoCZpEAWnUqiN/psvJArk0zetj5R9/88AfHheWDMFIym5G4suf3Vlvzhd++xY+e7nnvvdioFgvx+AKnV9QSiBEVa2JJ2lhiYXaFNYGUA5UwVBpS2IyI5vBrhzbhcv1whI1ASW4wSb2+lasUM4Un9JP4BlILmGk1nuc83gfcKHDOo+xBmXWkXP7o8Ps3324hupWaLJCO49RHqMswXf0vZzcccowz1jf0E6jVYc2niVpWo44DWWlxjtX0SykBOmimMeK0Y6wP+C3jVQmXL9jeUa7eUU1wsmkKNHFFUWLkhmlXux48Rc/5f7NxDeP/0JdjqJ+X9NNa1GUtA7YnbRYZmUlKGRv1KlA04Gvv/qC/+7vfs6ur9AWcqs8nq9MccZ3DevBholYRrTN3N7uOJ3OzLPg3aZpIcaIUkrg4EvGmY5+2KD1xJIjWS2wLk+M8RhVuI5XqTaVAERKquhWV4+7oqbK09MT43Xk1eefs9/fEm4cOc/M7QOREykXWnUoDDlnSkoohfAbWhX9rutgLpTamMeF4OQQPJ0STRlKfmK33XJzOGBtRanEpr+ntZ5lyWy2gf2uW00e9pMYXEbYlhDc+o/FOY0xis2w5f7uFfuDB/3I+fzAu9dHXn/3kYeHkWUGpTu0sVKxUPFeGKaX8Yz1CyZc6XYjzzuIEsX22KqiVEetSqqxXCVYr4nrprZnOEwlpUxKEoiogZoTacm06pDRWyOXRGmLPKfB4APEGEkxkxaBr7Qq9kgfNLt9T9c5zueZp6cJhaHvtljb4YLBOoUPBu9lTKK1YbxeOJ8nqYJ6TypZmMatkeIkDA8HqCJVXKsY49FKbJi+y3hTacnTYqKUZYWaKEorYnTRhmm58nB8S++V2Mud52534GlIK90fUk6y3a/i/kNJlY6CJWeWHBlTZcyeQ9FYkxBQdKI0R1lrVXFeqdW1JnSvNw8zbx4WCgJg8cZidKA2S6GRW0KpyrVmXj+MfDwldiHgKnwy+BiDdwZvNORGUw1t1rywmFYDgYz3lBIzUW2FUCvOgjWKZZk5nU4skyOjVxdmAOVl5t5YYdmaPxkKiAFtJWT50ONcWOer+pO07NkcUWr+YYernQGVqTbTUiO3BT0suC7gjSYtC/l4hZJwQy/xCM5SmyGljBNW4erJLlibZf41a+ZzJMUFNQw0tcUET1BR5hhRbqy4nGhkmuuI2tOaxF60XJmaxt8c2H79iq8/s7z+7sy/XS7US6JkEbTnpKDI9tpj6IzFrRwBratAg3Wm33X84q9/zNdf3dLKlULksiSezk/YkLl/KTrO948fOJ8fGeeF03kBBd4HUIbrZWKaFqyzuODY327YDhtuDoP4z88b2dQuYv+sraKtvPyXy4klzjjTSeaUqcQSJT3TaIxxWOXWhIf1lz9UyjKKnKx2aAzTMjPPopcUralYfU3LolJVFVqh5kaOjWWGHC2lNjprOex39J0llwlnnbT5WZBxxjxbFp8fRGkVFVI9DVvDdtPLCERXIJHbkSVr5rjlcn3i+z+84fUfHrmcEzlLBDoFyjJTWsIoydZqNnK3zbh+Aj0BMiIRO62hZsjZUool50KrgmIEMEoyunKVyk2qKo1Wjv1+w24biPPM+3ri+FRlrJELS4rkatbkAyVSopxISVrQ1laOaTDsDz195zk+HXk6L1jbMwwDzhmMzTincHZd6K0t5PU68/btA0tM7A47+iFgSwVTPvElQJHiqidVayaXLigWrFEYU8TbnxslJVrJVOPJBVqU4EdtEqfzA//8q3+kpp/z1csfo1XPYd9xd7uh0EipkBb5fpLA23DOMvQDxhnmZSRWeccucyZ3hs5KB3hOmdNyohLZD4ZdsJhmAfncU9K8/7gwx1VS0LIsqULAlcB5muRw0o5YOx6P8OFx4subAbc+nwqFN4qNtwyhErIkHhurZYHbRELZsgJdMNqTisGUilGGYAxOw1Qal8vMNEZ21mA1cijr+onvqquwLpR6BvxL2oEPAW0s3gW0deKy/HSw5k9Or7y6Qf/dh2tNlQWpTE1VhG3BdwonFxxtWdBpxnmhACltMNajCzhdKTQBOGhpVbWqtKyZL5XleqHrKp3TaIq08EbRSsTWim2JMk+0ZsA6qglMSyPGwnXOfHedaW8e+MnHJ+4//5q//eVfcvzwyPU3b+VlyZWUK60pIRQph0Nja8M1hW/i+d5sFLv7yt12YTx+yzIlMopLKpyuj/hBc7fr+PBw5f2HbzleriypMo4FELfNPEWu1wXvArvDlmHj8cGwHToOuy0+3PIyfsn1cuH09MDxdOQ6V4pSOCvnVE4ZcsLYDGaRAb92hG3Pq34Q3Z2zAg6xhaWcSC0CgZJhGhPzVMQlVVeL6uo6QzWaymidV7eUIS6aOClyBJRiu9nQBY1xEWMr2ihihpIhBIkQT2nBmJ6aM8ZUapbqSjiloE3BOEMjM01XlmXh6fSENZ7xknn/7sJ4VpQil0qr8vVLikBhM3Tc3Ac++8Lh+olcBUHZqmShpazENBAbJRlKgVosICF+NEURUgi1NmISDSe147AL/OXPPudmf+ByHjG85Xp9Q1nVA/McibHDGPVHe3FunwwRWgkucLsb6LuO8/nC49NE0wObzQFtDMrIZxGc0PibWchoLk+J7799z/t3J7Rx+ODwnaVFRQGxmKtGnBOtyKI1OOh7jQsZ4yasrtQkIvxaGsucSHPCOQ9o5ilTjWy+l1L45rv3TKdM/IvMj774mhB2HA4d13nhsi7pUkrUJl9vlaIzBuskQrs2xZQaxzGxbBwba4m58v/+60f+yz/9gVQnfvGXd/zyr7/kdrunZMf7p8ib/4+zN2uS48rObNcZ3T2GHAGQAEFwrFuaTLfV//8f9EObrqkllYoqFqsIYszMyIjw4cz9sB3sfulr1nzBAwkzAsEM93P2/r61HmZ+envg4TgypYimMfiGdQ3nNNOSiSHRjMGYgXleuLs7sbzYsd20VdVe6bRm4zWDk2ZcVmCdZbsbmOaZaZppVmNwaL2iZDpLZx1OK6x11GrkOxELpbAeVQutJVoJqKLFQECGX8sEYK00T5UW355a0ZIlZ1pNQKK2SkpNDBm/5eGqdZHQr25ol+kH6HuLdRVVEzVEDJnO9etWU1S6OWZqiuQYaKXQeYO2CgyE1DifA3Ge2O16tg5iOKOMl1qcKjRrWIuqVOUpQZNKZkqFZj1nv+Hn9/dM//ELbejpzAWvXj3n8Z/+nodj5PDzR4pa+/HIxtlo2dg7wGFx2nK5NXz/2cB+a+DNz/w4f0QPGrPdMuXGQzijN43pdsfbDw/88stbzkumNkNrwhMtOTJNCzQlW8XOMWw8zskbdlpmYE/fX3G5u+HpkwsOx3s+3h+ZxlH4sk3jjHSjVUvM84L3PcZrtLfsN5dsul5OUTpT9EJKi2yzk2eeJHSP8rK5lTMQKwIIdKX9SoS11KoJi/xw0Apdb9jtPfsLh3WJUhohVuY5obTl+vaSsBxEl16q1B5FNkFbzRA5J0JWGN/J/LhmIcinynQ6c7gLzJOhFbnKKyS4rXRmGDx91/P0s4Enn4NyD8S8AFJvztmToyKGRoqZkqUimpMwCmhtvebJ6do6IxXjDCFWOtd48aLn61cbKKIpz3URiHcVVOE4ZZZZco45VlnMVo2YaoVNu9n29F3HeJ65u3+kKs9+d4X3PbWllbngQCtinlniiWmKfHy7cLwXyHo/9PQbj/OaVNfTe1OktJBjwpqOobPstomrix7nZVGKkuVcKZUwF+Y5UmLm0ngUlmmeyASMV5RimRfHz+cT+/6gQieuAAAgAElEQVSe26vnbHcF7zXOOqwVZrJSi+iaKlilsSi8MSRtaLURKjwmxSk19pvGXC1/eZP5b//yQMhnHqeRrAzfvBg43I/8t//xA68/Tnw4Ke5OiaI1vXXoOmGNYrPpqW1mHI/kKqMk4xMfPx55PF2y6wZhOaREywrdHBYxviqt6Z3B7Xpq2JBDZM4LDdE7dQb2Q8/FxkuUyvTQHHGRlmjeyyKqkihFuMvaiWsLIwmI9aEnjA1gPUJKsoC1OFQitWVKyaTUVkPCb3i4Pn3WC7FKbylxpJaAVR7dNGnKtFhxRvBcxqztk1wI08x8nkkh4pzH9Q4/QNaRaS6UKoPjWho1JFpsNBtRroFRKO2opZGNItNRJgjLhL7U+OcvGN8vHOIvLI8Lf/yXn6jdlr/9B8/X337FT3+5493HI0vKOCVvI++szMEs9J2l32y5fLLj6yvD83TmUhtMTKiPgaIKRd/RcsXVStaO9+rAQxqxSdPpnqQNpWpyLqtf3eOsFtzbMuM6hOGaC2c948zEZjOy31uMmzE+0Q9N5IlVaozWKnwvMbE5QluFaVUJAMcYw35/xZwPPM535NxIwTBPlRRFd660ZHlLrsSkySUgQ9H8a2W2Vg1YapHM43bnePr5LS9fPePqeiDnkeOpMh7OxAS7vcc7i1GDuNhWi4PWBrPyNrV1WP0JdNGREhjbYaqm5EzKEAPUJFtmvaqNjZUt/e7Com2jGwLjfMLkiu8crWVKqqToGM8wTYlawdkereTfywm4rpzYhtZK5s61krOiFc3TFz3ffPWETW8Yz2dCODMtAe0sjkoqsMxwfKw4s1BLXdtmjVaUlC0Gz9B3nMeJx8OJnBXdfoN1hlIzpUYajWnOjNOREGbGaeTxMZBmh/MbjNEMmy2+G2SWqh2tFJZlYR4nGa9st1xfO/ZbeHb9BGsq43THOC+cxkpYIM2NeU6SfjEdqmpSTIS60Bb5ubjcXvHi2ed8/fIV3u9oSP7YO4vWib7vGbaFOY6UkvC+x1pFZxXFGpacCBWmYjiEictSOU+O94fMQ1Ck7Pjj68Bh/pEnFxMfPx758+tfmJPB9HtiAbSh2EJOmZwz+3nmPJ45nR5pzdLyljIGXuuJP/+0Q+XGxmZaPLGEyn3YECYDocrt0xi8qtxsPGXfcXeOhDKSjWO73bG97vG9nD69VXgtN8J5nklJRi1NKRqWVgolZdFqO1HSqE+JB/XrLysYJtMQ1VTNmZwDOYslxfz6IP6/fLg+ufE4ZymlSCA3NLxbpXZJoZrFeVEsK1UpORKXRJgWckpSOzUWZQ3aO3KSLZ/renJqkq8b5cO3vZwarPFoLTGR6j0twBQmSt+4/v4lYfcVb//wb4zjQs4w3U0s//rvKKP48sXvudpt6YzCq4pxmq7vuLzY8ux2z+3NlidPr7l4ccPtZxtux0eW//4v+FAZBoc1Cl3EsJCaYiyKkA0xGZ7gmX3lYBPvVeWhNkJp4NSadVPEHCml8vDxxDyPlCwxLO8N/eC5uenoN5VcItM5E0PBKI91ejWBaqyxdCtsmirRlJBOnIOUCrRL1FGaUGFRxKDQ2mFdFaGjsmgn2+dS5L1bW1uBLJpSDU1JP37bW7b7DS+e3/Lk6SVKVealUKrj+voFQz9hnJYXm2q4TpTessT6VF3U+M7j3EA3DOt8XaDZuY2o1KBZWknICFhiSV2vubzy3Dy1KBOYl4mUgaSwRWyfpWZqsYTFM40wTYqcI840jEnClV03uyCnV8lxilU3F7i9Hfj+u1uuLntSmpjmmcMxcR4LuVSMUTTVoUrH+RApZZHZW5Neu0Zua33fEULgeBzJCfww0PUOVCKVREwLtSbaGGgNUmyMx5kwNazxdBvJRna9x1mP1nZdoWQg03WG3Vbmom6oWK/wvcOZwjhH5uXMOErkrRYJ8Q9dz+AFmrIsgViFuHW9e8L3X33HyxfP2XRuvc1kvFMMPRxPBecsF5d7gWfHyMV+QOkCJPpuLce0KomSJbDknsfHzMPpntICc9S8/Zj55cMjpk2iZGoa73tcVfQUjJGFYVkCj9OZ6aGiWsNVsN7ijUNVxeMh8M//3xt++fN7dn1GlYlpzixtTzYXVNtjO0ecAjKkL1yiMM4y6oLpLV0HjUDOFmU1rRaoMzFnTqeF+0Oj7xXDsMP5AZqkBOr/xucQzuz/9nxFbCilBBlRlUqNhZpmahXLiRVq+v/9w/V8uKPWSgyiWBi2Io3TpdJK/tUHVFuixUCpkgCoVWFdR81St1tSJJwipVXioqhFY+1OwCAlQUtrj92hrCPXRmpQmmKaA6POPPt/vuTm+3/gn/915O2796QSKcpBNBxez/xr+IH754HDw4zKkd6AdpaXr17wj//0t3z19TOurga6wVJ8pKWR9hAATbOaYhSu02K2NKJCoyVca+TWUXTHzik2zlMQgEixRsRmsMJTHMsUOZ8i47hgLPge0JY8LaBhWNR6vXa0KibJtm64aRKKHgaB/moNKQdCFJWvsRMYyWDOcyMsZW0hyRC+1rwCmC3W9XgUtcjcq+Qk7aQkyzznFV2/4epqy37vSGFkmidO50eM6/n977/l+Hjg7du3PJ5GWjMo1WGsxFdqadCExbu7cPT9BmW8ZCdVw9Qsyz0L3kt9M+WEtZrtbstnL/bcPrVYFziNs0SMMpQqMr8UFaV1WDNg9EDvHdTGNI7kGMgqoY3M8bwXgtFSxLRai8xlfa/56usrXry4gBbXLfrEhw+B6VxIWYy61il5KTVFKpWaBaGpNXiv6DuhIqUsXjVjDMpqQhxZQhZmbc20llC6oFpHmBp50Rjk92ur8Z39dd6qjZIUh4XOO4bhhu22o/MduQZKnZjiA60mDucz5xmUGhg2lywt0HeZ3UZikiknYQTkyma44tsvvuXFs+c4Z/gkM2wVDJbBZpyKFGPQvWO/vV7pXZEpTFLS8D22NcqcKCQmVVl2jk1vefFsy82fHGWSiFgsMIW4OvAMg3bsrLA6/NBDs4Q5kJciWEuaaFTWuXSqhrQo3r+ZOH4I7LYKqxvzGMml4oeGGwI4uy6UCi0ldNUoY+m1vHCn6UQ6TUzbHV03EmPkeD5S88x//mfhzc+Vm9sdX716xdWVLKl00YDH+So1bf0JP8MKm4mktFBqoORIzZWWEtQZxSKAff0bNS/37x4IIdFqYXvh6L30xWtS1Jyx1qK9oWlZXKVYRD7X7cUYkAO1Fo6nI3X9Yi1Lw2pD123xtgpjEYvtHNp5gUCnSK6wxMRcM1e/e8nzf/xHTumSH//8A8fTx5UX4GkNytL4+PrEx3f/RkyVHBNOKfa7Db/79iXffv0MPxRO54/cvR2Zpwfmw5HNY+AmGHzf45rCtII24k/KNVFakSuEsyylci6Fk7acVUdEtqeqVVoWtGJOhWUKLFMkzFmyplo4B1dXPUYV4pzFqIu42Wtt5Jxw1q0yRfDOklOm6wTe25qlc5puUzmNs4Cel0pTFuscWsvG2VlHrEU+G2fw2pGD3AzCUliWQisO5yohZOrGc3nVYWzm4f4OEHhF5yzUzJObJ4znmfcfHsilSKzIKaz2iIG+or14441Voj5va2bUaoa+wzRDDortPhLCie1O8+LLC55+PuD7xLQsq6ZDxHa1ZgnwFEVMYAbPdnNB7wzTFPAWlsWwLHKdjS1Rimzy5fpZxFThLa9eXfD9d08ZOk0KM+O0cDhUTqcmVtYiVDetDaUUlLHoalfbhUHrzND5ddSQ1vOMYlkCJRY5VPBJhSTz4854jB5QTmF6MfMqL8xbaw2SVV8LAmuudbftGQaLNXJaogrY+f5wYp7PTFPGmVuePv1s9dUdUHlhu/F03hDSQt95ri6vefHsa3mwGkWO0mijVPIcqKXJWC9NtKVhTU9nBtEH5SLNrSb7EjVX8rwQcuTDodIviY6GGWeeOkNxieYNi9Uk4/CuQ5XK3itut57t1qOcJweQFZBZ/16FqhASV5hpIdJqoVpFo6dUIZ3VUvDaYptlHiNTnsgIfD3nhEVhjcBcUkmE1ghmh3Ed/dDTyKATmsy75UTNZ55/fktKjpubhPWXKOu5fRp44Qa0keW0UusuoSRSmkhxkpNrClAKloJmkWo8htR+o1q7JU+nPcYXetegRtJcqEEyZabbYTvZbuQi/bFaCjnOxAWWeaK1mZgXvO/QnaW3bn1zibRNeYd1Fu070IYYAylUprhQLOy+eMrzf/gb/PUX/PW/v+X169eEeKaVlcqjJBBUahVaeEWuia2RU+DNzz8RwntiG2VBc1wo04xWlm8vrnh6dYn3G5xK+CrAiZQWyBpaj+8vMP2et3d3vDmfOOqByXco19FRIExST00K6xV2EFVMSELZ8u6CnAMxSGW1lU9KYC3zS13XAbrAhMWjJItB7x1uteJ2nSWmAw+HkfPYQDmcc1i7euJJWDcTYiSmgNMd1grlvaS2zlolOZBLFoK8LaAnHu4TDw8B7z2+74hh4s3Pb3n58ku+ePEFH+7ueTjcUxUsAYlrxQQmU1ZYcWkgPnjZ7moruWSF4hJHy+Bc4fJ6y9PPPf1OMIm5ZlASsl/B9KAKcxAgdl5mvLV420myYvOJGWJW+LLMVlOWyI+wZiXg/+33t1xfddSycB4n3n+YefumcjpFchZgj0IAPjFHmvJoY0WL4zJdJ+j98zitJ1NLDBCTkPdRRfroCL+g84auGzCtFzC8b4zzSDMVaw16zbCWFilVImx9bxl6YQq0llflupQjzqfMOFZK9rz67hsuLp/y8+tf8M4w7AZurjZ0fUc5jdxc3/LdN7/nYrjFlEYOE60ttFIIY+TulwfOp5HtfkNcJL9rbU/EsSQpHnir0UEeeumcaEEeuPctUN5nNlVR58QXe8XWKrJSxNpTKpSsSPPC1hVeXHpuPrtCOcd0CMwmElDkVImtMeciyvaKzMmVkGBrVYSIKLWrRxlZWp5z4ZQbU8ksMZBbwmgtBD4qqmUSltBmWo0YKw0314Hv9MqRcPAhMIef8f49GI/vPX/zt79js9tLWaDJCC3nREkLtczUmqhpoaQZ1ZLE5lSiKUWMjWP8jWkBwwatMilOHOOZmBbazuNUL7xDr7HeobQXtUaJ1LCwTIGUFDFMlBqw3UDnLvFmQ9f71aORMV5jfS9hd+NZYmEOiWleiLrw4vtvePY3f4N/9oKPB/jTH//C4fB2zagpQNxGbTUx1rX9JfPoRk6RX356w4fXes22NbqcMKbj6fMn3NzeAIXUGs4aUgnonKAovL3F2B7jNuh+4NDPHMaFKRTyfKIYhdYJG2f8Q6NEaD24naa6jnzRmOeMNYKH866x6ffEJZCyJJiVAmcljqW1Xp8aQlGyVvr4xjVyKTwcP3D/8IF5Vig1rApgj3NO4latEZeGVhKIr1nhO4vrOlrt6IfKOI7EmDHacXG94/bZQEgnPrwbef9mlFPU/hJnRTVujOHy5oIXXzwl1SOljOT8qZ1VUFb65VWxljNkDqudRK2ULmhj6LzB28b+IuN6jRuSLDtyE3yek2qzVLwd4zwRlkqOmpAWwjxKq8mJME6EdjJzM9qi6CgtU3Kgrg+J775/wssvLlGtME0L794e+eE/jrx/B2GWD/qTMUFpAYVro+l6x6RGtJY42jzPLEFYrdbIDsFrh7HgvMG7Dd7v19+foEAtgi5MJaGstLGs12hTaSrRWl5fotB3BqWKXDvrAkooYiEWQtTkNLDzF/zu5d9i+y3vPnxksBd0tef6aos2Pd5ZtttLrvZPcVhOh/ecDx9wWrOcF853gY8/nzge7ri87rl9umffWQYD1IpvhTlE6piBSI0zdcp0pbGxcptzWWOaYWsa/gKeXPY0NdC0IpXC6bgwWunw3+4sT248290F47Bw1w6cS5JDU5JFb0ySJ64FqmorraqwFNGcV+CwZJYYCbWwoJlzlcIDGm21fOZKCF/ywk3UlgnLgtKGUBQsMje1RlFQnOeJVB9orXF7e8WLL14wnh8ZOiFczUvgcHigpCj0uE5jdF5HAREqFEQp9PGY+Ov70288uTZFipnzGKkqQcsM2uC3ms3QY718wZXyGJ3QNWNbxa62z85ZbL+n31zhnXSkla3oTkhHznqatqRSyHlhHBfO40jQ8Nnvv+KLf/o7+icvWcqGv/70Iz//5c+kcEa8N221hn4KKws7AMU6w5TNeWtKyEc4FJmkK66/5uWXv+P5Z578+mdyKuiNAwylaPA7uv3neLPFaUuKZy5z5UvXsaWxTYqZKpCM1lNLJSa5mizKcnmzZ3OluNePFGQ5cX17w8XlNYcPd8Q4SXnQVIxh1S3rVQBXpALreoyu5LTw/u4DHx8+yH/P7NFGTv9d5/CdobbIMi1M4yhQZqUoMZB1lWjY0HFxAeO5EJeI9x1PPtvTbTOPpzMfP0YOh4AxQuS/2BvmeeT+4S2xnTDec3m15fE4M88zOUe0NjjfCVXfSjOoVVlYCoHeYI1HqUpVhX6TMU7ytk1lclaUAsY67Cqds8avEOkGWIwWilIphWUSxKD3Qv83Bj4tg4xpKJsxRtH3nufPt3z19RWdh2VcuL878pefHnn7y0KMBoXB6jXgb+Rn0jmL94bbp5fE5JmnkfkYWGIG7aWt4z1adxgN1hk2GyOtRTMAiZgap3FkCQsxGwoK5zW+czivUaairZY/LxlnG1pJ9rgkGUOhGyEmlrmRFg1Lx8vPvuLl5y8J2nF58QSVjpgSscNAiIFCo/cbNCJDDI8Hjr98QDXPxw8nHt4tlEnaf3ObMfsN1/st++2AxpAGmPTE+XCkphmjE603UlRoaW02ChmrQ7PteqrSYBzGVRqGyWnuWqC2RmcLpJmygDeNmxvHftDM58D9obCkRFsyKIfSgkCUTpqS71IWx3GshTEFUqvkFciu60r2KpqmNBhDZd01GFkA51owypGrIkfJpwoRS5apaPDGY+2AojIeP6DqTFOeN+/u+c///BNWab559QXPn27p+0ZrC840nLGECO8OMz+9nfnw8BtLBNTCPGdytPTDhs4YrBKAgnyBFDEFclrIIdBKEnRZFqDvZrdnc/GUiiLFExVZXvV+h/OCI1xCYgoLaZk4zzMnKsOLW57+/dfYZ09IbDg+FP7zD3/icPcLqmZ5aBrRhtSV2VnXeZ9GgtafOsJKmZUtKpxBZXouLj/jm2+/Y795z+HnGRcG6pyxBioW7XY060nGUVrldLonng74JXKBkpP2sBPvUYOxC9zfH1mWQFMWp3uMVQxdYEpnBue5GHa0ZpmWyv2Hkc4ZLq4trM0T35mV1F5QVIahJ6SJdx8+8uHhnobF+Q3ODjj3iSWpQUViHFmWmRBFO955D1oTYgQlqubt1vPsswusPdH7gYtrzRxOPNwHwuJQaiP1xFbp+o5h55mWkfyY6YatfGm6nhBmpumMVR1GO5yTLGCuCShYDa0Jlq6hqS1Kf94osQavwIzWDKXE9UshnylNM85ZgBzGkmvEWNhsNmga85KoDVIsxPa/2jKNRFURReOzZwNPnu7ZbjMpzpzPM2/fn3j7NpGiolWZkQqvUxZKxhs67zC2YVzmcrdhu+k4MNHqLLNZXWWEowtuNcZ2bkNOjVZlkTUtheMiJgFjFd6tzAWrUbahzfrfMw1rq4DIabQi7FylepZ54XxOxABe73l2/YLfffs9++0lFM+2v2BM99A3ioks4UDNs9S9j/ek08T53R0Pr+8JqSMVh1WG3c4znxPz4wOnj5qNVbQlYLSi5UaeEy5HGoHOWFpDHoJaqqSohGoK4+SBmEqj1ZlWE9rCYC2bzojiJQXGu0joLLv9jsurDfqiJ+x7tDUsBaa0YLUHFCllQquUBrlCasJ2DbUSWpNbDYK1ESyIotBkieYdtUJYZqnf64IhAFK6yE00hA1FyIVQJdZoe7GjLdORD+8jp9OGEB0//Pk9//7vP2AUHB8eeHh5zXajyWni8nLPxf6K+2Pl9fuZ+7NB281ve7hqV6i9o6DxG8dua+ncCsgossgpcSGFQo1tdS5lamm43tMNHakFUp5J+YQ1DWctSskHkpZHpnlmDIUwLdynmbd24enmAnN1QdYDNTl++vGv/OXHP7KMB1pZxWLI1Voqa+uGb/2V1ej4iYUllWXZBlqz4bPPv+DLV9fw8TUtgdFuldkZujbg9UBOQvdfwsg0nymt4Yxm7wy9s/jtgOk6FhWwfaN0W873jWNILMuJrt/QXw701jJ4zXwYef3xZz7cHdm4DdvNgHUG6yTT2XV6bQcpastYY0jZyslJebSy0Bys8z1rDDkvhDjKi0UJCDznhPN+PbXLaMTaGeM69hdWEJHGUUvg4T5yuKuUZCUa01v2FwbXa2JufLg/UlXl5Rev8L3C+w1Xl5qaFWGKlFzWF1shN/GPaSOLGa00pZR1VGDIxdHYSglBycNJPnNR3bTSWOZFtNLWUXSiqPTrqGEY5PMRBUpZgScNtS41qQarK88/3/D55z1az0zzzIePZ959iMzhUz/diCpFN0lNOIPvPMPg8b7RWgQMvuu4uHagPOfzmVIk0WANEk/MEGb5szgvsJglFpp2aL9ybI2wM0orGCVxOK00zjaMLjJbzRCDoBBzbRxPSUhdpuOrL7/i7777B158fksthjIFluM90/E9V9db6gL58BF7GinV8fjwwHIq/PzjHX/5+Z4lW3zvuOw0+90lZrCMQZPnzPHuiOtgu+2IITCfF7wx9J0hpcYyzkzTiWG7oeuttAPVKlLUstTMsYBKGJdRurLbdJLySIlcpLRSh/X7vlLqbKe4uNxSlGcqhpoKx1KZYyXUQv708rOGTw7DVuRzNk3J0skYYk5ECp0RAlaZE3lewfdtzUiXhsKgdZWHLXJELrkyq8gSZs7jgbs78YPdHeDPf33gcIq0Gsnxr9zd32FNJi5nbm+v+ezzL5nSjpgt280F+/3+tz1c+65jNpXQJdzW0fc9tFF01bVQY5QfOOuIUZE/OehNo5CZwomaT2hb6LZyWqAZwmkhcoZ8JITIOVs+lsxrFdC3Oz775mt6f4lunuNx4sc//IHHj79ASetlP697W3mWisJHaCwrK0g2fk3+cW2yQTdNsdne8PV3r7h5uuHD20pIoC4tdA2dK7bbUrSViFg1dBWuhksW3RHiGeehcx7XW2yv6ACnPU4btPO488L7cWJ8jJjrjsEb0rLw8HHk4/3IkhXOGorSa/kA8dv3Cq29AFiUZVkCx2kkV411W0AMD8YIHWheZnJdBNZhe1qtYCy5LqSY6LxHK9nolzJLvMwqXGu0Fnh8HLn/GFkmhTUzzsN2t8X3hmkJnE+Vx+OEsY3tENjvHd3QsG5gO1yi6hFrpBaYq4BixFMki5lfTxooAXw3uep/Qso5o7Bqj9IZKISy0BB8nncGPVQh2xeHtRpME25FMcRY1pRFpmSgNYyyvHje8bvfX7PZVpYl8PAQefdu4XCQ+qPRdgWhgLFVonLe0neOvtc4Dyi1+rUqxoHrFCa0FXUH3micGwjzifPxAesNLnlCgVRk+6/bp+itkQ9FFbFvWIe3nWR4cyKXKgLLIvCR0zlyfIyEMXCxdQzO0w+OmirlfGb88JHl3Vs4HYiLmAuWh4SLhfPpjkNaSMlxDIagNixU7o4TH3MgXyU+2+949uQJg7NsnOXiynF5s2GcRh40lNQ4z4XlPBOXRYBAttFvFZtufYgphesH+m5HXDIhKkqL5KWSIqRZ+Lqmh5Ybqmm8dzS05JjPgZJm0rIwhsYpKU5z5pQrec02KyPAmbJ+gVsr1LzqY1Ay98yFmgu1jyjjaU1wjjLrZz18qdUwIQzYksTu3BSkGDidKvcPlZwc0zLz/r5wdxCSl8ZwXhTxwwItoqlMYeQ4v2N7kbm5+Ywn1z19/39+fv7/Cwqnmb5meqvZ0UghYLy8feUnQnQnmA601Ey73uOVJjcFNuN7KwprY2jFCHSiLMxloabAROFh0/NuqynDLf/vf/0v/Je/+68M3SVLdrz56S/89Y//RhofaVWaFLXWFSkn4f26ZphkC/9rUg2BMDR0k39iXceTpy/45vuXdL101ZW1sPI2jVY0p0klSKxIW1Ad+KcYO2J8AxXxvZw0h87Q2gafEt7B9vKCm6Fwqe/56/GOeDzRIXGoTXHszFZC1VU26Zt+R9dljCnrydOhVI9SjcfTPW/fP7DEgut6tFEYZailMsaFpiQa5VyHsT2KhNYjoJjmhVqECWCNJDiWZUQpuyL6BKGolUUpudJaqyklMU0aazr6bkPZWg6Pj7x5/QBfXHJ4XIDCft/EB+VWpmerfJLXqSbAGG20vGhRApppBqUtyivJp9oeYyyKQljOmHVWb7RCtULUM7k4dOtp1ZBSopQiSQSj1uptR8qCr9vvev7m7y958sRRSuZ8Krx9s3B3L1dsrTzaWQGiUDCm4bwSDbeBrhMNi1ZOkgcqQtVoW3HekpLAiLTK9OuDuja5aYQpCSLadTgtzq5WFa6X8YJxiWYTTlustrSyUMpICpLnNcaQcubxYeHwfqLOle4qks4z4Xjiw/s77paFd+8fqO9G0knx458PnKI8ZHrdSGnmOEmrriaF1x2D77He0A9X7DY9u05x2Wu8Gxg2nqErxDjRVMX1HTGu9DAa2ms2g8X3Pa2JubapCMZgaiSEhbqWNWLSnI+N+dyoqdI0OK1RVRFmB2pP13tSmnCuQD2teuzGHCqhKtJamS4lCTS7iqTRqFVEajRNa3IV6IrYCIos0qu4trSWW5TcakRjXypSTrKOVtpaDq9UIOTG3SHx8Bi5PywcR4gZmipYpYgKFBVrFEM3YEvPaaw0PXN5EUAtNPVb2QKMbJGaV0saOgvVUkKmklFGjuG1qlVdUQXhpbWIyZzkYGtrq1JDeIx5zixNcTBwHBrnq0b1W54/ueG7V9+wc3tq7RgfMz/86x+4e/eWnDMNeaM1LcqQTw/QuvMxB84AACAASURBVB5fFTLXE/QasixSFY3kRYfNBa++fsWzF1eUckdJFesl0K3RaKepZKm7VaEtVRq5KeZ0IoQF7xyd69j0ls56krzYMEkxYLBNY7sdF9vM1I4sx0jMmi45ptqItQlhqQpfUrVGjpFWxJce00KpkXGcmcYE2tMy8gbXRoDcpawsB491g/AnlZwea4W4ZGrKtGLxHWgrLxlYVgalwfsNNzcbjDmzzI1lkVjb5dXAZ89uKbnw5t09p7GwLBOHA3jnubru0GaFOVcFtUlLRUmtteay0qMkamY7h9L9+iK0cl1rAmQpJZNzQmmFNdKxp1VUV1DakeOGEvW6BJJaa63rrL3KF0SbiPOG5y8cX74aUFVzPjfevVt48+5EyqLvqbmhmkErQc9Zp7FOXqrea7zrsLZHO0vVIq8rLaO0wnt58MRYmeZAq0esMvhuQwNyCKRacapIRMh2XF1cc3t7g9GZcXwgF9C1UEtkOlfmcxHViJKCQgiZ+dRg7rh2F1xbw6Yq9DRy+uUDh/cPvLufOIyB09j4cJQb35TBoXA6M6XEPC7cbnZ8/eySJ8OGQRk2Q8fNhWdjRko8cZwXHsdE9xDwu5nhcoPWjjBn0iLks64bJOivFOdxorVAo6K9Q5lKqwIXqq0SlkZOlhghxUrFYEtD28aSHpkK7C4uiREOx8QUCtp5eu3pVMEuGb0UdM4Yo6lKEVMWqLeWcU5GZqdVKWoutJJBZ+a5AJq8Sj6lrsoakYNGIdUmRhAli/BGJebG4ZRYgoyu5iWTsrSttFFkKqnIMsxaQ66alBOnY2Y8NyiOnBKX179RrV3NGW2r3GyGHu09ZU7UnGimUZ0QjUo+0FTC2LJ2zxW6aAoVVdY3RcxMIZMa1Gz5WWf+bE7YHm4un3Kzv+X7V6/4/OIapS0xKX7+yxt++uEHlmmUB3TVlNZElrc6fKBIvU9mA6Jn0NJCskaUE5qGVZrr6yd89e2X7C966lHhlJJ4jZDO5O24tpxaSdBmUAFrEraeWELC2QFPxatInirjEZZZkZulaY1ShSdDz43eMi2BcW48xkabE5s54kslFUUwlXiSsUCplaUWSgnipCqJcUrUZmAF8taqJYDNJ1ulgaYppaGRF01bXzCtWZYFTqcTu51nt7c4V1FarutUS6Ww3fXs9k85HTPvPzygdGZ30dheLISQubiSQDbNMgyWfrAou6ztrCYPeKXIRWaZRjsG17Hfbui8JywZAVHrFZdYKTVJ8yUXplnAIa01UgqULOJB6ww5K85TJYQgRDAtUj7nNM1ZciqCabSFy92Wr7+5pLOWMFceDok3b4/MIbK/vkTXntPjwjwmGVNojfWyROw6iV811ZhDwLS05oc92lbyqjFxrsOYyBwCrc30fouzfoVsK0qI5FxwneX57Wd88+335BL48PE1KTTGMRPzhHMbwglO9wVqwJlFygpbT5c6LruOl7e3XF51lPOJ1//2H6SHxpv39/zx/QMfQ8U6x5wyRVUiVdI8FLmymw7vB1LO3B0PDGha3rAfNiRbeCiaP707EE+JJ4Piqxcd+6uBJRnm6USLic5rKIWILBep0qrMpeKVp7MDqWZyyiyzPFz1eqpcothCVCkYV6lL4Dh9YNgFSjUcT2dqbRjX413HVjX6NpOapTmJZeYG52lBt4gCchGGRamK1OTgo1pBtUquyNJaq3XBKd+DT2QrGR2JGdk5h2pycytFGL3LUtf5+9oeqJ/OtauhVmlyE8IaVaOrJcXAMn3g/u7A7c1vfbiqSF2v/XMonI73+KgYjKOZRtSZgpDVN31PbwdSLuTUSDWRdUKBmCBLIhfPvWr8Ug/8qU3oa8d3T57wdH/DzcUFX17f0jlPBk5j5If/8Qc+vnktDqyV01mqfFCflL5aKVa2OIqGUZKB1FRaqb+2Lqyz3D55xpdfPZfN8Hbg4nLHQxVyk7OOhsSJSiykJaNIdBuwRip+thg2TtPZRsuF5VQIjwnTFLgeM1zS+wHGIymt71pjUH4tOWDQKfGQA/V8JjwaumELvcVYzbwc0UYRUpZtLACFXCu6FIyTeWGrMuSP00ROC8pZvPV41wOTXHkSHA+BJUT6zSW+h1Iqmm6VqjUqE33f88XNBTef7Yhxoesr03RPKvLQ8dahlcbYQm4ymnFmEKODlc56VYa+27DbbNj2HUY1lmViWXU0YFGtk4JJmdBGZmnaGEoqnM5nYgzCYjCw3V+hjWeZKjlL4D1XxB5rP32BFNY3jPG8/PIptzdbaiyM58jHuxOnaWF3NeAsqNrYXXTywVXJERtvQEmW1XUWLQUxSpCTUTZZlNs4weVVYQwIlapSc2KzEdqbsQZXHbVWnlxf8+XzF8znkXd3v/B4fuD+44nTx4VWFJuhR2fL8lDwLGw2mieXhr1pbC56ttst9w/3/PMfFo7LI5tS+XzzjHMLPEYhVZ3n8/8aCylNtobSBJHnKEzzzM9xARpdbjyfBjR7theG12PgL6eAzpaWNeqxcvjxkRIb83kSDXVutNTQXoBH3vl1z2LIEWkxNkuOCOqwaJSTWxNa8I8xJSgRo5s8kB4zqWVi1hjrQHtiaVALg1EU78hFWo9GQe8dm65DowhhIqRMXP1frTVJYDRhLHziXciCU+Sdm80GrTXTNBFTlOhm/eQjM9IvLGmFpYvO6BMloJYq9hJlxLVnQDtoLZCIUDxtNuS4UM8Pv+3hSpSOdWuywMqxoJUhWbDK0K/yuCUUUnK4RVODLDiyaVSzBvy1YtaK9yrxzkfm68aL/VOub67Z93tUtVz0Oy73V1RliU3x1x9f89MPfyQukxh8sfJlEG2rVDzXiMXaGvjVfWO0wSqHUU2urbax2V7w4tWXXN7saDqTVSLVsjrtARytGXKSem9ZCspUUoLHx5HzOdP5HZ2vWA1xqsRRKpdVazSVjXNYlQj1iLYZ01l0a3StcWEUZTXjXhTLuSy0Ra5hfhjkJJUXYkmkXKltDbavdk6lGkb3KKUpLVNTIKdIOGeJl+1vcUYIVotq5Axdv+XZ5zuubh21jNTSUYqDltFW6p/zPJNKwJgd2ogT/pwDuSZaNWgkU2pcxTiP0bJt997ifZN8rr5g2O3QVOIyS0OvNEpToDSqaVpN1JpJMZBzpDawvqfrBmIqLCGSS2az7TCmoXFcXgzQMqdjIC7rCUTarb+enK+vLnn++TWGLOmAh3sex5lu2NIohCVgtLTlnG9obXErv0KaWZVUDR4LxQqPWFVajcSUqEVg0i1rHJrOaXI2QuvKFWssVju22w3WGLa94/7De6YpEOtMmCYOHw+UgxY4u7JcDT3fvNrw/GrP8yeXXO4vODwceHt45JgCP04Lfz1OHB4m9lazxAPaOfAdrhRCEEFfzk2WYk1Ob6pVYgwsGqzqqChirXRLon8YcbPi9TTyOFc2dsuYLW/uEufTwtbp9ZACBaHwW+NxWuO9LGBDqIQQSaViVE9Jn3iqMuIptcnCVUkSQl5mmpQQY4fSK3jaSqA/J865sGQxd6SVLNaaVOR3g6RtFqOZYmIpmVQzbQ20h5AFpFKlgCBoQBmBpZRkAVdFdIkS5mutMjtrta63XJGZiqRQoQwYLU1JuQXK30VrqcVr/QmCJFjGFH5jzjWcGyFFao1kVcjWEDeOyRs6Z7AlESKk6ohJM5aCyTKrKs7QvCF0mgfb+EBidIXr22te3jynaxsJBzdF5xxPP3uB8juq6jjeLfzHv/w79+/fCBRj3fxLRa5RWxHIiZK31qczHqwCNREgrHOXglWOm+trXn39gn5jKWqklIVxjqAsVRmM60UiFyOQZAOvFNOUCLHH94btpcMP8oEup0ZNEixvzpOVo+aFqmaogVoyqglAGZNwqrJdTQIC/xg4xsjd3cjOeq46sUvmRZZ28kA11BpRLYPZkUJCotaSL8y10JQYWL11bDae5fzAPM5sB8fNkxc8ezEQ0nvmKVGiLJFKO8nSScn/tyVJLpAqbiRl1vYSSnKDq31U1DgKZ2C32XB1cY014mBa4sw5zYQwU3IQ9oDWWNPL9dlqVAFVO0oqxBRY8ogNkQai9DaV7aajpLieYhv7XU9NjRpnYbcqA03JosIP3NwMGH/HnBSHaeY4L5RqV5BxxWghJOUYxZmEzM4aRgLsVVTn1vbkX//uTZCGNRNZmNf5u6LHmA3aipIo1EyrCoPHWYezcB4P5GMjnieGATYq883Vlmcvr/ifnL1Zjx1ZlqX3ndGGO/nAMRgROVRVV7ekB/3/ZwEtCSqoUVWdXUNmRnZWDGSQdPc7mdmZ9bDNmQUBEtBBgAD9ieS1a+fsYa1vvX11w91+z673HHrFrrcs1fLdT2d+/9Mn3v88cTFXfo6V6ZoxUZyDYc23qtbJ4RQL58ss88kWaWtctFagakS7kcP2hqVVLtOVjzmyHBPq1DiXTEExmiCRSa2SyVRlRTutFEaLb7+VRMmKGcv5ksjZ0Tkvltq4JsiWzMopF+GjyVQSppW14tXMpRJi5DQnlHWY4ChacQ6JUxJXVsiZ2qoYajSQIyrOWD3SKQ3GYo0iVkU1oJ3DmMQ5T8KkXee/6jlTLAqchtZkUc1fGK25Qs1VzBGKNSpHpKTCLFFf1EZagUWTkqXrPK7TYveuGXTj+v+Nc/3/P1yfSiUbqF1HsR16v6HtOoIqWCpDsrhhQGnHYiWCt4ZM05Z2GMi95bEu/PlyRGF5fXPHV29/hdUvOB49p/MFxWe+/ttXHO5fgu4pwfBvf3zPn/71n4nzeZVRacGDtRXeXdu6IUQyx9X/63cDqmwGjRbb5Ou373j97hXGBnK48sc//Ymffv6AKZnSZK5srMZ5T/EJrR2pWuoSGbZbbFdx/UIjU5ZKzRatLVhLsxZtHc41yKtIedjhB8vQGZYSOD080R4XypSZWhPRPHCdM/l4xY3Q9U2CHAFaFVlJK2AULWtQDe8rrUWKli22Kp5h2PDrb7/G20RMTywh4buOm7se4yLLOfL5o8CV719s6MdVE9xEOysjlgpEWRpq0aFaYxm7HmMbpS1Y27Pb3rPb7ri7ucM5z3V6YAkXLstVYsBpNO3lS2ssKCsT4VpRWKwfsEWIVCVH5hBJKTOOA7e3B1pZCEv+0qF0znF76+h8x3RdSMWQa8UC263FD4GQErWMPJwy53Pjeo7kZFdpWxPSVZElYqlNqhNvvsirqBatHRBkSaYs1nTktFBzWQ8hWW7oNrLRA1YlUlxoylBXvm9tkTRd6aJhWwr32nB3f8PbVy/59dsX3IwDNYN3HcYqYqy8//NH/vn3f+Ljp0d0hTpFuGb2tcf6Dc5bwrrgaaVSamWaZ/G/Vy0jsyQpH6azVA2xZOa0UKylecMVxRybLKONQ+WJoGYqorwQWaMUOVavMssc1ll4I5TK6ZqxZovTo8CKWhLzQ5Oxj7cWdCLmhCpFpHetUSkSVWQNrmukClNYmEvhWhqXJKOOXESL2qhop1GtMi8LLTcoVWauLbOkQNFgiifnSiMLKrGJ9E+tzMBa65ckV+P86uQUzoAwPexzdUZtki1XinTGypgvY8emNHnNbmtNUZ1GEoobTYl1/hcdro8vevLQEfst1TmaMzSvCXmh5cBhdNx2A1Zrlho5xZmkFMOwQQ2erCvLAvlS6NTAzf4tRt8xT5Z5qsQl8vLW85uv3tKt0baPDwv/9I//zNOnDwKlbWV1WDxrA9YrBRk5UJrcmqvAWRsBTmv9F+/xuD3w9ttfsd+P2Jo4Xyf+67/+C59++I6/4YBKjhwDxciLp53o6XSyQKTUROeg6w3eepKBZGTQbtaUSOMtzomyAN+j8Gg7YA8jvs1oZ9DmkeV4IaTEkcYxF64xMJ8T/a6itEB8NRIB3qpCaSVwaN2JhMmUL+i7Wg0lwtgP3N9uQV043A58ejhTqmaarlSViUvjcqnM14LrAv3OSNRx0dQMuoozTDK2ZEnljKX3PWO/kRnW9iVdd2Az3KMNlLpwfvrMZXoipoVYM6yLJ63lGSgMrRku5xPXa6L3G7abHmc7qo+0IpCZkis5GWIQ33nLFqWtyOOMp+8du+2BWuB8DXx+ekDbxu29RbsFo0bO58pP3584HhdacRhlwUBZL2NJYBBjBbUQ8kIu0vKVJMsMVYNQsWpPrGCKZVtGbKos00S+BjqTMFqxAWLVEBu9EsDMfE1sleZXLze8ur/n9YuB29st27GD4yOP3/9ArIr+sKfbenrf86sbi/uPr/j53pKj5udHxZ8+LVynRAoz2SkupbBkKQJikjZUa5EgllwFUm7FWBJi4poC5XrEOf+lCq3es0RN1SKsn3NltpV97xk6x3bocFbLZb4GT4oMSrgRm9HhrWY7WGIIlJTR1mCwtNpQyNinJNGHG23XPUOjKJFKds5DaSwhk0ulVGEO5ywzTpTEflsjSc2lISMIoKlGaZJpmzPkIoyOWhMoUTjopldehJaFlpZEXVhDLaW8BqvWzlCBapRa1oBHtaa9+hXDmKQoWJddAvsxjMOIc5qSA6n8QirWn28Nbhyg76hWNIImS3S0cpXoC7OXXPG5LMyu0h92HG5uKDkwhTOv7vd0dst89gz+FSUNIhUqke1Y+dvffs2Lu9coZNP73e+/57//4TtxHq1rqlKqhMY9t//PMlaef/x3NHClZHNtxJ9ltOXm5RvefvOO0Rt0CTStqUZYlKmVNQ00UFyHM2aNMBGeq2oLOU7U3EPx5OjIRZPWD9YZQ997/ODWG1RYrNMiBKARBVZjRovaCuegyyOuVHSMK8FIEy+O0hm0aXjn8K6n5oakxXZY51EmApWiFdVoUix03Yavv3pN5yGkgvOWnBPzXMhFk1dNqzES1nW+JjaTZbvphMKlnm94vYrsZdSxG0fGbsPY7wQurBtaGy7zEUhoF4glkBGLpFaiWfgyjlltxyk1Hj4/8enDwtBHtrue/VZiXbzLhDlQc+Z6uXK9NmoxaDzeitURZpknKoPWkva5Oxg2O8tmW3FeEybDhx+vfPjxQgyVzlvx7VthNVTk367U2v41oU7VIrHQ8ZpQW41vmZYMeUrUHLjxhk1niDUSiZKSURtGR2Qns8Uay3bwhM3CUxe5HQZ+/frAq69u2N5v6ExH+PTA9cOREgKt11xOkccjfPvmDX/1+o63Q+P96Hk4K7KKfCgTp3ZkTqIS0cYzOFnmtlZJZU1gWFtZrIMm1ZpQdITZoWtlg2bsLcUrJttQ2pBNTwmRVMXtYKxMW2vJGN1W1oXBKEel4hDnlKFiVEKvjY5Zi5lUF1JKsqREipxmZGFYSybETFzda3KeVZy1bHQPBtoSKJTVhCI9VG5STxtY86w0JSbAYI08R6XEdNIAp6wgQLXCOYfRcjGo2qirO6tIZOuXA1gpMcEYLQ4YjaRpdF0HIIdrK+uZ0vBO0/fC481Vcvp+cczLsRTGmhkJDKpgUBhVsZ2knlorkcDTEmjNMNKxbxvuN7fM5UyuF5y1vLx/ydUOtDpAMQy9BhJ3h4FvvnqLsQO5Wh5+nviXf/wnjo8/CVey8UUL+lymr2/vqtcUCchzeY95/sBYnUAK6wdevn3H3YuDiPVb5fF65loKtR8l/VQ3al1QpcM40TrqUtDV4I2FDMt5oZVG7jvyItKMZ5eYFmyrtB5K4l5SbmANMUVKWljiwiVHSdrMipsEaoFN8lySg0dDtR63VQxOSZNeFG11+XhnMEbTsGQMMVcUHa9evOT+dicRFFXR+4HD/obpshCXQoii2Bj6A1ZplC4s14Sh4eyaPFolwnnoNozbnu2mY9OPjH6HMwPXJfPjjz+ibCWXwnY7Mmwd2ipc10GKshVsiJlk/WAaIvC/nCMfvj9jzcLN3ciyH9lue7w3dG4HLXKZj1ynQKsdlsasA7kszPNELpW+7xg3nm7Q3L0YuLuztLaQg+HnDzMPnxrO3lHyTM6KmBLD4HHOrO6wdbSUpWqqReDcne3QoaIvC70xlKuGqBh8zyF5AaTvLPbWQBYmbi0Vr6Vz8MawHTTRGtS7O3bDntv9gXE/UI2ihkiNiX7s8fc31E7z9JR4/8OFy/VIjJnbXtMZz3U58aePH/j+UlgsXG1DN1G60GTJaayhUoixAgrvvSxgkMPVrpFL1joGYzj0Xsh2JXPXazbeEFPPxRhG1ei8wzsxc4Asa4w1OG0xSqOsolCJuaCyoq3PWtW2Lq4M1hoZ2dUmM24FGLFEi4LHoJuS1l9JJWqRZXlvNaWTuO2ySqtkSacoShIpnHEoo7DN4xDuQWkVXStmXWR3xoOxqzxQ0HgWuXRSieScvyQNPE9glRJNPihUFmB9WWN30hpPr7TQ92rNgEHpQliuzEsSyZd2v+xw1doQ08xQG53dMBhwSgDBuSjO54tAhu0W10bUU5FW/r5hBsdgPbUYjNkw+D1WWYbRr6F2mW+/uuGwP1DRhKD57l/+zA9/+j0lXFB1nZFUkd7Aenitc5m6lvXKrFu+1YOuVoScoqCVY7O74atv37E7eJSKpDLxeHkitkZ/t4djopVZ5FSMVLMVLKlqKG0xjJQ0s0wnatOrp3+lqq+iZL1WuiVlahJOQo6KblsxaMJSCAvU4mkhYefIPma6pbFUz6QdaQ74x4TLjdpliioovUHrHu8cu62n1UooSqKxk2K3ueGr1y9xVlqlWpT8n8ctiol5SpJfFRdyOdN3I+MwQIYcElY7Bj/SbXrGfmSz3cm23goEPObAZbrw+enEh49H0aWWws3tLa/f3jBsDNtNB0pRlgWjpEporaAwKC1szOVcmY6ZUhaW65V4t2He91gPXecYx4799obeN5bFkIJQ4LXpsZ3okTdDT99rxm3l5uCxVEI0PH3OfPfdmeOjwtqOzeaG6bowXSZaVWw3PUatceZRmK8WAaVQDVulGGuiXTQhVtQls3GZXfNsu8IL27EfhJy/zAtBT3hdccaT8fTe0tvC3CL9fsNhv8drAy2Rc2OeZ0q64juD6hxzyBw/F6az4Wk+c50/8jdve0a/J+REyw0dM0stTDVha6EvmmmeUSvXwNp+TagtDF3/RTDvnBaddBYurLECk55LJaTGRhecb2Asg4eO8mW+r/TaKRixjtdSxLZbxSxk174xr99xhWz0jVKUxpdZpTZu/Q6IldWqRmcsyTRKFsiSVYZcDSU1cg1QI1bXFVxkBUJTZSSgKRhbVrW6WRdNTcDybdWwI9Wt9V64uTWjmkSNsy68nZPKu9QiUr7nylsLE7hp0b6mtJqmWlvHjGLuqa0KcnXKkrBSxHjS2V+YRDCMPcYV+sGz3WzYdQZVI6fLmcs8Uyps7R7f7qjBMj0+8jh9RhvF3W/2DKqn2h3W3VHNiLMNqyrT9cztofH61S3GenJVfPzxkT/87h85P76n5STuH6T8l5nqulQoZSUbrbeKZgVKrLa9VQCsAWsdNy9f8Prrl7iu0liYw5kwnem8IXaF6pIIptd02SFEUroQQqZTRiAowZOWgm4Ry4LvB3yvqXn54khrax5PiYEak3BKTcFpR1E9WWtwPdE2jJqhwpzBYxiVEu/0tdCKIhhYTCb6QLfZ4MceoxuRSMiREAqldtwcbthtO5HBFEUuBZqh7zqMzqQ0U6pHGRh7zbiBzhd6axnHjt1+w25zYOh3dK5Dm0ItM3PMxJrIbWZZMqdrJuWeaZIqAB3Z3SZ8b4hJcG4ii5MqOOW2XoiRy2lhmSQts9ZGWDJPj2dyiXSDwzlJyN1sPJvhjv4wkFJimi+ksoCxOOPovMGYzG5j6LQlX+HyVPnhTxMff1wI0QAzdb/B6p7eQloCoSW8M7L0KdB13UpSUoQ5o1IiXBvzpYdsGbJEWY9kUAu6WozeU/We0xJwSnFzP9KNI9WNKAUb77l1b7CDwxnFcjxRSqO5jtYCpS2ErKinwHWaKLmw349MyXB8aPxbO3MYK/PFc6NGbkvh8XIV+r2CS1wISYA8xorlWzXhmFprySn9pQjR0s7qWriUBMUTm4Kq6SxMS+KUMqFkmlGkVCk6U72Ai5SyaCXLRrQsdlRbxypVLl1JmlBre16oVeaO0rFIVVhaIxfRI2u0ICJLJVdFWtUez2hBq1cra5N33hj3BVWprCW2RlkCOReZra4tvdEG06D3EuaJ1jgryc5U0dvnLOm2Tgv3uNAopaxjDRE3FyV2+lKqxGc/d8pVyfLLSDeca6WUKKm0RsZMpdRfdrh2nWXY9tzsDtzub/Ct8PQQOR1nainsuxvqUXH+dCFeIF4ycbow3ii6X+8pvke5G4zZcJzPTPOEbQrDha9ev2K3OVAxTFPmj//0B3768x8paZabsyo5WNcbs7XnQ1MJ0GH9Jdrhhqoy5JeSv6IxuK7n9buvuH+xx5hGKZnz6Yk0L/S2cS4nzhluWo/NGh8yoTxQ1UypjVBnvD3guxGrLWnJzHqm60eUFf0pKlHLgmoGsiw2qIV+0+F9paZAiQ2KxVvDbn8g2542AkNExwK54JRErLRkmVIG3/gUA+flI1M+clWKpE9cpkiYHXf7O+5uJBtqmhaa0msKhCTyvni5YykFpTq83+E8OCeQmW2/ZRgNzhmc6dBKE2Mi1xO5nkm1khU0nYVSrxyZQm4JVGVerkxTz37XrdEhMq9aQmCZM8vSiKlQama6ZEIMaNdwpscaTVOZaVojdFpdma2NEC5sto39ZmQ7bpmujWlq1KxoUVrilhvnj4H5qHl6ylw+NVzuVnWCwSZRW/TKkeQtRxlDbzpB51GpUTbDwje1FHpac5imaLrhdWUgk1RHUZ5Pp8r742fma+DNvnJ/17HdbTGbAylWrBvxY48fDNfTJ2IudMOI320ZSk/fG+Jp4fI4ka8JoyrjmAgPjuVp4GMMfO4iny4LISgGq9ElQ5LsKJHvKWopzNNM34/0fS9VlmSKSxrpqgFV69I3ZHHkxSJjB+UcTTvaenjmailWlrPRgq6yFlcqy8Yfg7EDqAxVDtFGxVqoqdp0oAAAIABJREFUq3ZZCp+2guqfrY4rOKmKNb5KD766ChWZxlylOm9GfVle1aZEd45oU61z9MNArUWg2SnhjMUbWZzSQCN7D5QUWkZrKXiMMC6WeV5lnAXd9Jpa7DHOYK2l1EKsiapAl7KC4OV8UShxp603h1JrYjXr4rs1as6/8HD1HdCIIXG9TDxNVx4ePpHTgskdj+8njt+fCOdMyx6nHNsNvHm152bX86QK2ogcJqYrj58+4tH87W/vefvyLdrsiMnw459/5ve/+29czo8yF6vSapRnnKBivSGeGQIyNFdmLdtZPe7Iba6VRlnHuL/l3Tfv2O0GGokQIx8/PzLPAV0Dl7iQouF13bKPoO26tFAKpQqxBPreMg49SycYQkMlzk+yzbYNRaSkitGeFoUhqZ3DjQZtM8s0M4fGPFt5+bse123JyuLDhD5N6GmiawXTJOLCFUWvLBun+e76yGQV07bjcTpyOsFueMmb+1dse8sSJsJyRRtPyZoYMqVm9jdbDkvgcgkyf+sHDocN236k957KzLJcWWJEm0gphVyuVCJNG5rSNCTAT2PRquLdOv6oiuU6k9MAfc88RabrxNPDmeNTIMb1ealKSuLlHjeDCM9LIy6ZVJJUD6tcJrVGrVesSrgOXuw8Vfd8TIkfjguX60LXGz7/MEGumDJQkqLLPd0gy0sQClPTlmoMVQ9c5zMpFKxx5FZoPFcmEIoBM1Cwq5a34bSmtx2b0bO/G9nc75mS5frzz5zPJ+43PbFUcD372xcoZSmxrcBnjbY94/4Gv9ky7kep8ozlvDzyaZLZcFaRbmcp0UL2PMyJnx6e+PQ0M7gOM3qSSfSd47C5QdE4LzPXIGzceV5QSg7bZ+VMbay2Gpn/q9Zo2pCapiLuQ2M9Qz+AjagSMVlGOLk25lTILTM6g/EG7+QgbGhpr1ectSwZDarIc/XOoQvUIKxdvVLFVJORAUoRvkgnpdU27dnllalKkZWmKi2s31xkZrvuTmIVCVxu7ctSKpUs8+DVJCRnRsRqI2CkklAUYkmEElf7eEY18G7AaCd2amScWBs0YyjGUoxU4W2VkjWqjD+soyEdbq0VVYWtof79Zv1/5HCtFdKcmZ4e+Bh/orWIdYXeeJ5+CDz9cSEdF1pTGKXIeuHV25EX7+5JClLNdLZilKVzHTFkDruRb79+R+dvqW3kfJz51//6HT/+8D0xPWv3VtdVLev9sUqAVxWWtAaSQf5FQPCscUXGBdp33Lx4zeuvXuM6qKpynmc+Hq+k1Y9cleMagwQsksktMaiBpivaZFCZlK84Gt6KZc66hmozhh7rPNquLUNMpNCoOLTXKLOCSVokl0ZeQHcN2zkynsVAcQo3aryz+CoLw5Ybw9IowM4Zups9p43hsWZ+fCqk1PPut+94/eoOpTMpREJcMBpK8cQkbTnKsdkcOD79TFSZw6FjHDZ0nabUmVQmYr6KTVdbjPbyMqkedKNqAZuXpKjRMmhPJpKrmAHSVHn69MTpEa7XzHxZuJ4Dy9xoVbSztYk2sxaDQy7ZlgqmgKqgYoJo0FZ0sdve8NtXr/jtyxv2JhIvldgi3y2N01PC6Ch0/KLYjtAp6KuiMwZnRJ3QtCJrw9LgmgPzLN/PNkirKYkDa+pEAIzDd5ZOw95q9sCbYcNff/uSb955/FCxww1vvn3JD/+9Y2MVw80O2++wvsN6Q3aNWo1ELW+2FKdoqhHjwrIszKeLRBilQqiNqjp0Xq289sxTuPIUClPWpNYoJhOKwlTLUB07XdFNsVTICkJYgNX6bcVBCVpkgU6vM8mKsSL3as1h6nNxUjCt4tZDeBWFi/QoJzSazdjR9UK+UwSRVSWRRRssIHlnyli09ZQ5oJOoUlIsq/pEngkUVC2rckfLUryCt4aIJWeRWmK0AJPUs8yyknIhFRl5lFpoWjYuuYjszzsDyqwKEOFQlLp+71olpUzMks6rlagEVBWKlTYWZRpWKZnlVoW1ErRKa2ISUCJDGzaervOUklHzTAjCH5HQyl84Fohx5VguiTSfUT7y4rClr1t+Ps4sR41K3TrvVChTuXlxg98eeIyTgCUWWKYzx+MJpxu//uaeu5t7avPMsfGH3//E7/7hvzHPE6UpShVQAoigt3ypVpGDVpn1ASie4YI0Idloo9G6YYyi60ZevnrF4W6D0gKafPz8mQ+fz2tm+ob7zY5PduZSF7bKYVcOqrFKSDiqEpYjoGhVmI7OVFxvJb/JeawDqJQo82DtDN1GZkBTzKS4BrNlecANT1VQXMLWSGfXC6iKkYGqsZ1GZU2uBY1l2zqMgYf9V9jtHb/91Vf0HcQsLXNYFM7JQq+srZUyit12z3azcLleWJYL05wBK/QsKlVpQinkkHBOYfUWZTWlybyvZZhPhYcfP1GDw2hLiJEQKtO5cfwkBo+YKilkUhDE4Ngbkc9kS4kZcsPQ8F5hh55uOwIBWlnhKQZrGgfX8W7Y87rz9GUimcr9pufORWZVZZbmt4S54rPmZtvxerNlbzytQGiVYDRT04R55rpMFKXwfkA5S6MQWuNymfDW0XUD/ejYDQN73zO0xqZGXtzuuLkZ8F3G2oDVF17uB/a/+poQFtx+i3YdsQRMM6hmZEmTImGZiNOTiPCRRORO9/i+xx8c7tpxOUM4Z2q+kusZpw0vdzt6HTnOCwnoTUcNCTUF/OgZbU9n5dBx3opbrYnUMGXZymsl6pq6Lmwshm3XE5JEq6eYWVQRir9WOOskKcEIEGmOFbRm1wzNGKxtWKPQTZal4uOv0m2sMTnNPEsfn/9NRTSmdtW5BuH1ssrfWlNihihF3l4tRoiSxD7Lus2XBOZ1sbTuXLS1stdYK3Zj16hPLfzVUArzPNNoK1qzfrG3q5U5oqh0XuF6I3NTlNDmcsXWSjNlJbep9RBuov8m47Ri1w14leVcXM0Hv/BwlZjdGhY0UnV+8+od9bzh+/weXSWSRGxjGWMc3eZAbJ5YzqSWeP/zBx4/v6cfGr95d8dvfvU1zg3kVvnpw2f+t//8f/NPf/w9vY5Yke7Dum2kqS8tnEKvPFdQRuQeZh1us0qy5P5uaDTbccvrt68Ytx26ZUJIfPr4wOMTbLY39IctL+4jl9OF+XMVa6aKlGTFaqcdzgZCmJhioZaAMxprFc4b3Dgy7jbUFkjLLDZdEn7wdBtDrIkYMgqhwV+XRM5SVRhX6F3Bd9A3g6kGqiMiscNY0Flho6Jb5Avab3t+/fU37N+84rDTtLrQmjAEFB7vBrQxLDFCWCHF1rHZbnk4nnl4OmHUTAhOXgbbo1SPUZZaruSUyG0BB0mdJRK6WOJJMZ8SVlm0k+ogzEBThBChWUHQNbkIxrFn63toimQUcbVCjt7Se9nujp2hcz2dtYyjZ9ggqoJj5PMff+CuHLh55RlGx29ee2pq7NpC323RjBwfZ/rB8/arO/76m9eMqnF5nPhwnPgYIcQmVDNr2Gz3MhJY4dS1VmLMaMBZjcGzsYYbN6BjoFF4nM6YnxOX0NjvrNDV0hVbE8YLdc3WTJwW4qkQL42aNbVOlHaFOmFaXrWnmmAgFcuw3fL6zR2dT1zmz1wvAYOhV5JWoNsCKmBaY6MNzhsOzuGdR2nDUApONarWLClLqODK1TBa4Yz6skXvjcPTaGEmPR+uLRNVExbnc2lS5UK0tsfgia2wJJESaiOx96VATI3aNM45mY3mhK6NTGEJjZQqrYKzfmXyZpF3qbLuTcTwIGeRvNcFWVbnkldzhAFtpKLWBqoEbnqn2G62gOFyniTHrRR0VnRmnY0qYKXlKW3Wv+Mv+vda68oYNlQUaYVqa6WFslcrusmoAsAau6o7tfBGcsQpw+AGxmEU1kotlH+vsf8fOVxVKozGobseawxfv3jF13fvWJpi7x+ZTKTWTEZoGg1Fqo4lGYoWyMbTw4XTMXB3e8Nf/eoNNze3KO24Xgv/5e//lf/8f/0dP7//NzadYes9227AGyXLhXUEwDp71UpRVw3bl6z4toIE5ONEa3DGcXtzy6s3r/DeQM1cp4nHU0SpLQqPMo5Nd2Dc9RzPgRAjQxOaj1VKor9RdH4FpTSFsRrtxalmnMI6SKlRaybmGWyl20gA2nRJhKDY37xgfzvi/MLj50CpYK0Et20Hx9b22CLAjWuaKS3Q1vakIvlSrUV0cby82TPuDVrNMsYoiVzSqimVqOpaRCqSckabhLcCm374cCQdHa53GKcYN5XD/o5dtyGbzPn6yDxNZBSmyxjl6e3I4D3sO8G91bLqISu1RHQztCyf03bcctjv6ceemArzvKYFlLQKy610UDVhXeTgO17sttwcPP0Gnp5mLhfDbbdh9AOb3Y5xaLyyG37724EPH4+kufHpw4XwdePl12959faO7dCI5weMURwXRb5GltjIKPa3NzjbEeZIqVlCFJvIsobOEsKCyjAoj6+KOVXm1DjOgYfrwt3JsR08zgT2VnEYNd1giO0JdXxCu0qaMuenRMmJwQY2O4vz9otZIZbMHCO5Slx673ru7wz2omlt4HKFy5T5tJx5jBNLK/TArR/Z9R5TIVSgNrbW0Iwm1JW25py4hrRsznvhV5JyozeaXmVqDKQiB8SUIg6FtTKTldRVRbXSJnunieHMfDkxa4/qB1LL1NyoucmYYdiILGmWtvuaCk/nSM6GwVl684zITBTVvhysjWdmg4wUGo3cZHHK8yiARqsrOL7KrNdq2G56Xr18RYyFMCcWArlGYqzYzsqirjVaE86x0sK7qCucRetO/ryeEyk3liVD7zBaOMGKilHPTFiBtjtr5Mwpa2y7AU/DAbbrMN6z/NLD9RbNt4c7ur5H9Yo3rw/c7npigbd3B45/PhNbRtNoWZFzZQ6r4Ng3jBnYjGC15jff3vP2zWusG8jF8f2PD/zv/8c/8v1PP7EsC9dQOTvLfszcDCNb6zAr/ajVJmSIda76lwEsq4lg9Vcosbt61/Hi9SsO9xu0lmH36XJkiY2h6+hcw5pGbpCaJo2KozEMU6WGJHr4JG4pY5C5mlFgHHhPVZVUJkIQV0srGaUKzkMqE3V2zFPleir03cLdbsfL+y2twHXKAkNRSSRk3YDRHpcDm2CJZ3lRWzNEVcnr5+uUpe8UtIlSI6VUYlkoNRFioWT5Gl9PiemaZaHUJJlzaI55gaY7bu/ecHu7I6cLNlQ2ncIMHX0ZOWVFSgbDjGmGvnlCUBAKSyhMAcJsZTkA7AZLZzz7fuTNyzvG/YYPpytPl4mYLSVl4Qb0Hjf0dH6QKtFnbgbY95ZRB3bDQJs7phAgZJ4eT2xvPIdXX3G4f4v2I7dfLZSp8v6Hj5ix5+7NS5SpxOsj8ykSw0JLCy5nuGSICTdICJ1SAuP2vpMioLTVogslJsoSmHXmp88njqGIRExHfvp8ZtuPbEfH/Wi4rx12mlFpweiEN4qSFNdrwqjE7VahlCxLlPLMRTOt6MiaT6R5wrQjShVa02jlKdpxTZlT1QS/pcSZUcOtV/QKllQJKWNqYZDwL6wCWTBB0g1lJN/Ka3BKEVvG6kbvLLkpjGrkLHEyrSrI0vUZq6lo0ZO2hHGGnVeMTooWafOl4rTa0PcjzveUPJFVBtVE12rkYtJtwSuNM06WUXk9VpWI/uWAleq6KomozqvrTDb3qxyqiM1UaY2iUEtjmhZSlKrSGL16Vhq5NkJ+npNWEf4X4QgLIF5/OTcUMgJptRBCWtM4EO2zVmJ1VZIMrJXEKmklagFNpbMO3aDljLIrKKf8QrXAa9vxTX+LcjvMFl4dHIeNQ/mRv/lN5PGHRx5PMoNLtZFV4TJPLCFgO42hYzvCy3vDX/36GzbjHug5Hgt/93d/4Pd/+J4lZ7KS+WIMUVB1uaJ3OwZn0a0iE114lnhoyVVeXbDPB+9zNWsYtnvu375ms3NolYj5wvFyxPmOveml7aBxuWYucyIQeRo9t8WglyLOC1OpvUKRsVqhtMf0W/ygQM3QIjkq2fqtWLI5zMSpoO2WvDjCdeEBqaq6cYP3MM9VEjNlt0xtkLUhOyvWWdehl4RetXaFhjcd2/2B5hRhmYUpWjPn68Lj54nrOVEy5FRZpkqKVWZRTT6PrRnZvf0V9/f3/E//83/i7mbkpx++4+Hje3xNmOJw7SW9z7QBusERr5npFKgT9M3RlKU0Q1aNWK/0vmPXG15sRt7dv+TubsfHy4Xj8YIyI/1gmduZFy/v2W88Tiv244G7YcTWhRKP5CbLBJRi043shwIxkxdFTJapbLFtS80K3MjmxYY33Y7zfKFo8Mow2i0TG0w7M+jGTjVutEW5nnNYWNpEyYlQErOeBBHICm3JBR1nTiSit3y6Tny+JsysGXvI84zhxG4cudt59lfLQGGjCq9vR65x4fh0ZRi3DF4RU8FcgRJAR+ZieJgjznsG29BFkdOVkhu1dVhtGTuNdaCKkVFODmxUoSuRSiPIThfXJBpbNeiMoap1NGYkQUMrjRVx2+p+klHa4CxNFZbqcdXgjcMpK+YBpchNkQq4mulsY9dbNp3GeXEE0hpFZk80BUuYKelEqQvadHS2Y1ctRs10pmFMXrvLTgBmRbqGmNY5plE0DblBzJmQolhtjf0ivaQZcUvlTG6LoCpT+eKgaq2uSRzy85TzaoMV+60c+pqmxJGnVxiSMQpnJaKbJt0XSqr4XBDwvHZ0z/pepHP2vsMr6LSoJGptTDFS47Tuh37J4eo2bBipxjF0hZvRsxs6dKf4X/7XO8bxP/DTj0cePiU+fzrxEI5UDXOY2SI5UUNf+M23b3hx8xJFz7JY/vDH9/z9P/ye6zTjXEdekxprq9RcYV7Q1rJvnt4o7GoOMEqEu6w34LPRVSE3jNIKZTzj/o67V3f4XgOBaXniOs8M/YhWA0tMhBh4PF64LhPVZpat4YrBhBk/J6oROLMaZD5DbXhrMUYedi2CLlO1kVNinhdO14UlarTJDN2AtxtySBwfjnRzJtUBVeTlU6agm8hBrFOoVAhxRpVK02KhLapSjINhi+o6TpcLT9OZaa5MOXI+TXz6cGK6ZFIsONPhzCCXR8n0xjIMHdvdjmEYOOy3jBR0mNhajbu9wdC4Pl6oqeB1ZdxoDi9fc368cqEwGEu3ZI7TiTgHXN+jhg2Hbc9htGwdvLrdMWw7/vzps7yovbAux9Fz2PbcHUZ6q9iYAdcU33//M8fjA9++fkHImmueuN/d883ffCVVmco4N7Ismi5UeY61oSz0vufT5888PX6iQ9QCDx+emC9ndFnQKdDT05zwTIlNFAu5rku8RigyW7Wq0bUCKXOpC3OeyUZTjeZpnskhQNUEMtfq8CcYS+WgG7oatkPP3Co1wTj2NCUQms5YKorOWva9QEc6A1p3ZAWXWAlLxXRVPnOT2dSMSppBW3YKyDDTmHNBaQEGxZxIuaxvFlgljsHaGqVEKvUvkeu1YVrBothazahGJHoevHYolBxaBXqtsEaWjqUgoZhNTCFKNYxTopuNkZxnnF2wzqCrpRQ5wP3YC65SZUKqMt9G4EO16fW3lEmR8iXplVa/xCnVWr90o8YYMQIUGXHlfOE5nLA2QUkqJZSzWgtWPculJNmjUVc54KpSMAiLVSXUyoV2Zu1e6l9A36wyT1nAVVRrOOsELFWkkyxALolKWSUXv+BwvXVbTFUMtvJi33PTb7BK0yi8eOm5u/uGOfyWn588P31c+HQ8slBQmyNKnSAnXtxt+earr+m6LaX0vP8w8X/+3e/4/sf3AIzdiLGa6zKJFIsmrdR1YoqB0VtGq/HG4I3+UrLT/iKCsK1JDpM2KDdyePGa2xe3IhpvkWm+siwF7z3aOM5zYsmJcTvw19uvSDVglefsQE2J3bTgs8hcXKe5lpncCqUspDhTa0ErLxKuVEVYXBxxUUwXcM7hlcXrnkKgxiaC6VaxpdFyBF1oSdiYLSd0ShATuTSSskQFAZiU5Vrg/Z8/8W/zkXNciE0Rc+X0dOVyDLI0cz1+3AoirWRIlaQSyje2nefV3Z4Xtzt8WZg+nekcbHe35JApZoEOOrcllUiaGzfbkb0L7K+Kc3Z8pTzvP068/3BkdBu+efOWzdYyzycSjePjkfefj8xB8o1aafTWM50zebmy7Ry1C6QQ+fHzkSVrNqEjlQofZ46HiTevPS9ejLQqrajJjXo+cvr4yHI+4b0F5Tk+Xcg5c02B5TxxfLjSYsCj0CpAyyzzRAyJ3vco7TAa+nGD9h1TDEzzGacau6HHWcN5DmQyeE2skEJE14ozoktdAFXl748p8nhaGDa3uE3P6fSZu70Xs4mSlrSmjDaZ3ehEyVIzKWZCgzkWptigiEV2azz3naVvhUFpdtYRlGHKEGrFIfyM1BRhtYRiNEZL1LoSxT5KNby2eKXle7WOE7yVZOJCg5IxDULJTCXToRiNuJVkZ6LkGbbEOFic0wJXqc8kKbGF07QwZIHeQlOWJQRJErBGjDNtNQCxhm9qQ1aKJSdiFWWC1voL7Lo2mZEau1bNVdGQMZRAa/4ixWzrWFCkRJqK/QKWl8P1GfMkLk+9mgNylmWm9x6rxdWYKygt1LTncVqtokpw65mjrMzqU87UVc0A/HK1gHUW3zVevOi4u9nglaUVKduFZ6iptadselAbbm5fENLCOf4ObWE37Pj6zTv221tqdVwu8F/+/g/8/T/8jsv5RE0JA2y6Ea0N5+uVlCKpFmrNhJi4LNBbQ+csnXN45yQFNRVSLV8cIp21bPqBO3dge3vHdr+RBUYpPDxMXK6F3Q1gNK7zKG/Z9j1NjVwujZg1y0ZRt9AmS0uOkhr2YSYyQ2eYdaYUjW5G9IRVUTIoekrzotnLDW01rQqewmiFKpWaxYliWhbxd6mUWIkpkxBd4oLj0gqXDFNszDMobwjK8fPDmQ9PEfxIVYppnghXS+dHNsOWoRux1lJbouQFZwxb57i/ueHlzZ43tztudh1xuTIvV3TrUFVjq2I3dqRZ2tIwZ8J85eXtht5v2e80G6cwvvHyZsv90JNmA3nmh/cnYgM1z3x6OvHxKJxO11kJkszC+kyl8nQ5sagjrSmJLteWD48TTzVDKDwer3x8ytx80NweMq/ubtHKcvxcuB5/IE0PeNegOUCg6tOyMJ8magJKwThHNzp0WKhzoaVKqbJEOQw9u5sbnuZAiAtNNYw3hJJIOTOXKjEiiJqgVUVvBg4bTTd6UBozF7ySqJZSKg+nM/0wshkG0WTrDucNVMEpthohJtDiNgyxcr5mHq6JJTmctbRUoSgGazEuY1rD9yNzVizLTCpQDZQUBK2i5ZWtKaOUWD210WA8MU7MIQvBrjTpgkoGB80Kss8pjdKO0hqxFtxaZTelwVoZMZDIORJipjWDaqvkvEn6whIyRhmcqTQdoRWUMjgvkJcUZcGdcyWnRi0NZ5wYBloBLXlrKsGXM3Ldo7TW1thx4SO0L0CZNXDw/+HsvZosu7JzuzGX2+aYzCyHKrh2ZFMidfX/3xQhiv6KarKbl7e7QbZBG5RLd87ZZlk9zJ1oPciEALwAgQAiq3DO2mvP+X1jVNneXvnW9Fqb2X4NqnXPRRteIk82AV1SPaU3tChmyLWRciU1HUHU8oQh1EPWGI1/NWuZc2FdlXGLeboFQ8rftf66t1x/Erh+1eF6y9PzQNsgwlo9p+g5RW3YsHWNS1rY9z2ff/KGV89e45yOA3751Tf83T/8M1///neaczNPXYHC4Dpa15gapByhVXLTRkYsGYkZaxLO6tOylLJR5NsWULZ0YcEeXnF4dYXvNJs6z5H3H2Y+3l2Yy3sOxxfsD0fmeMcS78j1gree690Vp2nh9ngm0dPWnuVuxp1nOhsZbCBPGsyWp8CzQQ9Xf+CuFe5NpB80DSCm06JDaZSoQWicYF1VrL/zZBMoVZgjTFH1HQlH9IFohbV5ltqYEtynSrMdw3BUB5QMSD2xPxwJYTMM5ExMhdPpRC+N15++4TiOuFrI84W3D++5XE7knBjGa8Z+pdaJljNpEVLMxEUoufJxneiHPX7sWfKJGB+gGFx2xJi4i4/84eGBS24kIzQb8LsbhhyJy0QXLDf7I7t+z/39R1KKiLOEvqM3jXlZmPNKGAZs77nESHoovH88c31ciLFQouH5YVDGahIujyulZoxtFJOZlkydFuq2TF1dJRvD0AVummBNYV613kgxPNzd8/7xxGO80FzhdFaws/c9DbB4ehGs8xgfOHrHs1EwrmOaEzZOHA6eT653tLoyT2ckR65HBykRF31VFzJiwCHkVIirmotjhiUllpS5RMPYLB6h1gsC+OD0C2ksqa6kpoaImApzVvMqYsg5Ysj0dmP9WkuTyiqWJUVKa3iEIJZgLJ3zNAMtZpwLOO8pTlUr3ZZ5Ta1oxXVVf5u3mlX2Gzi65EJtlpQ1ikUrzG3RA8hY+r7DBXQptOo2RDv8ghNHtcqKrU8zUwRTFWCOaKSrtIK0ppjAtlXepaJspkatG8rS6Aa/1qoPBTzGWXzQQkVLlZQUz2nEYKzbfGhPsa/yrd0k5bzFw7SEgREFz5imTcu+p+t71mWFrIvcWrMSyjA4raz9/z9cP/3hCw43HW60FLNt4xDEWFI1nKPjfhXOqZJKpZTEZb2j1pWhP3B9PNKFkVI9Hz5G/vbv/5V//bef6XLJO5wzWpHLhVagdw7TDyyrkEtWnXbT+JWCE2DJhadi1sap0FBzg5aF/dU1Lz+5wvtEK5nHxwvvPjyw5kwvuh50LuPzRf+HdUfG/XOCdEzzW1ZvMENjvwsYa6l1JWTtxUtpSG5kk2gDuMFT1pUUT9gSuDp0DEbwVfRVNCWWOFNy3DKDltVBC47iPNV41rXyQCM1aNZRSgZnWZPhLglzEaKxPCyV07Rymu/oQocgLEsilgeQB6DgvTCdHzG58Pzmmd7u50gLluUSKblxPunNeZ1PnN1KKRNxTlA64qZc6a3hsgqP55UoYOB7AAAgAElEQVTaFsTONFmJsdKa5zQtvD2dOcVCFK+toTIjogFyJx7JjbTMeth5YaTH1ExcJ+KSICuUQ28xliKZhPBwWZhzJZgLO7mwq43eCbZ2bMtrLmuikRXyXRprhJwtKWmvvDcO8RZ8prOQqawVTjHivEKV57QQ10lBH85w6EakoYe8VJo0Qr3gotObZRFsEfatwnpmTQvLHBlaz2F3ZBBDWVaWUgkBWtUtd9pcW8Z5WhOCsxz3PcwBkkWk4YLV9lVR8WWcL5xTxLhGjzCvOmOsNFKayXWl90/NokgTwxRXYinoS68heI+jYqvGk6yzBBGs9RRjCEYPnLxMJAqxQpwTplWCG8CO5GZUe1OzNseKoRForDQSORWgELpGaVa/g0WzrqVkNTA7pzXclKgl4QQCQjUg3jMDc43fmkX0e71t4I3mwnMtm43A4bezTMcDopxicSqatEKuohJCZ7fsuyrbU9YBRauVVovObzcWrCAY4+iHHu8tS1yJWY2zqRYkJWLOmtSwDsFtOV626fd3OFxvXl8hvtGM9oWbKZhmqdVxTnC3GO4X/bCf55nT44lUPnA4LvThihAGoGNZLD/7t//kn//1Z9xf7jfQ7/YmIBrwzTFivSN4ixElI811UarNRhRv/5ebM2IQox9OjUpYxt01n3/xGS9fHLGiuor7xwdO88rh6oY3r74gBEPjHisrQiAtjhwb9x/fscwnutYQ28hdI9sOkStOF0erhi4lfGrEtiKmMPRQJcN6Ysw9nR0Z93uM96zzTJk/UpaZWRzFdDhvcJ2Qg2MxHUkcS6vU0GF8IyXh8eN7lumRh0lYa2Cuwnm9sBZLzrCuE8lnvPc6I8uVMkcOoePKeTrjcZ1lkAYpUmNkmQzLJWrLpgRySjQuzMsdXRi0wBAjDw93UDI3uxHvdqQqUBLNTKQWyRiayTxcJlIxgCPFSqoJYypiKyU7Uq1YZ0lr4ZQu1HUhtIpYYaqFaUmU6rFNFc7OGVwQYjUsbaQ3gUogxYXpYWIuSdmxoafzBoxWfqUWUnUsWcjFYFuDqLXlVi02NYIRRCpr1m1wE/05atIvdxNoNRNEl5ajhSyNmi48956X+57QjdTSiF2glkgpClFeU2VNhpYLvt8iUvkpp1nJuVKyoTWntgMqkqJK9aLCbtaauI8Lj0XVJsPWOozaecS5jh4oKZPJiEnYlhnthlKsSq9CMoVCNU73A1UTNAoL132FkY5UK2tM6JnTEK+c23mN1JyxxiFWD+JUCufLgqFRq9tQlIV1VuOz2jG0yxxjJmcwonPgUqpyXlHltpWGNxA29gC16HffCkU20Mv27zVp35YNpJltcb0hB9H22fZPt0Ok0Eol1qSz2ZIRAWccPgyUXLWyWqKmdIzdEkcKlfEh0IWO6+srxl3Ph4/veDxHStOqcc6bFbNCrplgIFjZRg7fcaElG95M5WwNh1CbZSrCKTnuV8vDnLg/z3z8eMf97T3j7p7rq0Tne6SNpNTxq1+/5+//6Wd88+695guxGAwlV2LZcp2oGVOMwTgF3e6M/RYBhqjWAhGa0duzddsVtoEVx6sXr/nh97/gsN9hTGGNK4/ThWYsznRbTOMjub5jcI2cLb/6339FfCj4scc975HRUOvKalZWV1l36lgfl8qxVIZFXyVdBOaIawVrK6FZomQaCed1+L0Ey0NyPODIuwP+aoczkSaNFjqqDVAT1oN4g5fA9dUzzszcnyZuH86cotZUjYGy6gzviatgtoG888rO3TfP6+MbghPmeKYtM3MtzPdCc4HSHJ6KsxOlVR4ftUVzfX2N7QaGfSae7yglgSmIbSCFy7Sw1kaxBtsL2TqWOSE2cBwDIo3UEhGYUmZOCUNlsJYgerOAzCowVVjFEbpeQc3BsR93Sj5aEtUuxFyIKTJNM5eSkeoxxtEV6AfteRupzGXlHAtzcljjMKaSYoQmWDE4qdSsNKeYjcZtRNGQtbCZYKGkhXVuHPqePliujiPlAmNuXFtL54XsPbdJKKuyGDrfsW8N25ShUWxToHn1alG1ZmsVagC/VX29tQLLPBNXYdwFltx4yIa7NWJKpoZA6Du9waWKEcthDJiUuKSF0e4J6AZ8KZk5FpyxlKeCDQqytrXqIeIdzRiWmJBmSE3fAGuNLDXSecEbUb6DHaBVYs6YdcI0o23BrQBgJWtsLGW1wwq0qrGpmjU26Jyl1IxYw27YYZZMLMr67bzV3/tat9wr4LR62opQRbPlubCZBoyODiuo7kLrtzRdkelBm3XBte2V7GYiKVWhLjy59p6IXWZzZIHqf54y0F3AB4ffaFlmYxa0pmUos92ABdSO0LRunL+r5gXZrtXFbFU2nRudo+GUOs6xclkS5/PK+RRZ55lxPGMFgnsGDLz9cOHv/vGn/PsvvlJ4rlU2wJNKJZeitthWkaqdZGscJuhvbs6JVvUXAprde4LYImxDaEsIA28+ec1nb17TB823LnHi48MD93ePzBMcjoXePEBJjLbn/dtH3v/yI9QrXvxgj9vvSf6WtFwQV4hG+NBm8B1XocfYjrZWwsNKZwTnDOIL2RSKSyQSKZ/JuaN0A3/A8rss2OOR/fU1k1HghN1eWawRvBitlF5W1suMTcpyKJcL+TRRUt2AFYWdabh9D8ZwWSdirZjO048BS6RKBWuxocNXQ0uNOF2IpXLKlWo7DsOe406XKbvR6uwzNa4Pe571njoIphaaG8hVJXrFGnA9uQlTnMmmsraVgOMwHuic436dmOdIbY4+7PS1tQjSHLswgrls1t5ASitdqHSDkOLC6QSIpZAYfMHWQlwrs2+cS8OLwbste1y0KJJzIWZhTo3TJeGMMHaOZVGvWfCCtEgwjbkISywUY2mir62D11ljKQsGIRghSCXQOErFj0fsnJEsLOeZ7HodZ5SFuGrKZWcdo1RClzE+K8FfBqSh+vDaNkKUZiZ955Fxx249k0pG2grOImGgxUzBMBeYlshSGiZXxGa6zpO8Y26BVrR8gnWswOwqtlmFrjRD7yzBQccGJTF6Ay0xI8aDDxhnNFJU9fvbAON7bXjVqGUc47SVlyvOGry1ugRb1OTQ7WDoHS1b8qozdJpBcekF61WXhGizSZLOWZ00POrWakYrqkEsBaGJJbWiCXC7vXQbXUDlrNYMBbDokjJljW8aqwekt24b5VXWpKS3FFcQi7W6J3o6RVrb4PutkXLErsLjY+V8hnVNgEfYGl9tqxWzSUG3JZleE/+f//h/p2JJQySqIrqoBXHK8LgY7s6F06y5u5QKOa5cXzc+eTny4uqG4+5TplX4yU9/zt/8/T/w9sMftw2bViJr3eRh22tCyY2E0vG9DdtvweZ+snXbFCrM1sqTFE1UA4MwdD1vPnnFJy+f4y00MqfpxO3dI8uc8f6koI0VDu4IM3z91XvWyWO7wHyK7M6wuwnc1sy6znT9gUM/cMmO1Q/c10TuPftHi78UHBkZCtVBNYrpnkvim7vMQ8t8TIbpcEO/30MrrOeJnCNIYxx37IaR87JyOU2kdSZOF2zMuJaxa+JZ79iHithG6HuGvqcfdmQa7+7u+OOHW4INvNgNDDQO+1F/hjwBBUpGWqEPnmwqj2thTpEuDzgJhE41Hs10fLy9w5QTR5/puo7+cKDf7Xl4eM+lFGgdUis5R2KLDEfPGAKdLVxOD9Rm2Hcj3hmMVFxLbPgxrK0YC3tx1OawKWLrSk4RJ5bLWpDq8Z3n9fEKUqbGxOncMA56FznsArUJ0+WyzTMNrQVMK0jNam+tghGHdX5bTKjMr2FpUshVQR5ZBGfV8JCzwaSJzgV2PjB0DmqA6jE2k2NiijPOZnoqtjMMvsPbwNh39G5lt6/0wdB1BieNWgwVtRHQ/oTpK8ZQjGXoPdEtxPRIrp40Z8iR4ALD7sA5RvI6c7RCb0Udb81yyZllnegQKI7FCGsDWSe8NHorWntVfRURC6WokmVrRUmtOAvWOTrbMZ0TU844Ms4KwVQ6pzbXlgtLLnTe6BIxFlJUilXNRo26GHJJlKYjjErTm28rxHIhZ620GgOWhqkZS8Ubs+XEKwGD8WoJplXqJrjECN5bhEq6KGwdjMJkjMXUpHYDo1VZ9bZp86y1xtqi8m6NNreasPEGZKvMZxqVlCO1FU2RtKaY1C3p83RDNqJwKKunNLV68rf82u9wuEqzbEQT/WAny0N0fLw0PjxMqkXOlZYTx/3CX/3lJ3z/y5cchhuce86//+Ib/vrv/pGvfvVLYkqEEKilkGLSJ1bwCtDdngSlFO35No1NWKs3vEoj10zNuowRIwQX9DepqWXUe8fzZzcc9wNiCjEtPJ5O5CS8evWMF28aXbdgy5He7Pjm17/nd7954DQJkh5I8og7FF48H8A6kjg6Sey8pWJZnPAwesrzvZoSPp5o80Inlho8qcJtWXgf9TVvcQbZ7xhuDvSDVxqwBGJcSXHi8njmfalIs3QmYKUxhg7vPGU94+1CSLDvOg5XI8N+pB9HhnHEeM/rV0e64Lm/n5GLfohibdBZZKsq5qT+rK4LtM7wkCbO68zuuCfs9sT5RMkzpWQ+Pp6wfUQcODOwe/aKV29e8Gy55vWXiTk1zsuJSxx5d/sB6zzX+xuu+j2PH+559+EEfkczgRwvLJcEXafELyMEvyc4S+cbz5zOAqcceUiRlIVDs8jcgII1jZoLsQq5NvKm50hrodVEXitGOoyHfRdwVx2NjRUgliaOaVn1i20Ma21YZ+hxXObIQ4m0mhmiY/AWbzy2CL3taa7ndskwXXjmhD4EXF4YXWZ3PNL1I94NOBPwLFgbORw9Y+8wVGpesK5TaloBX4SUdJmYSyPnhc5mDoMwLZUpVkKLHMIWis+6BfdGD0tDJRVhLY2lrEQDqTTSvCB2+/5UjaHp4sojRp9ruTRMK4whUO0WG8qJUoTQKcCnWatNqVboisEL394CiwClowIpF/z2VlnqyjJXSl4xkqhZb3TGWIWZVI1GWWlARkzFWeUfKOwEclOuq9v4A04aRYQiUFHLAVYwRt9u5em4kqcFd9sg1tt4QHeblKwHaf2WpFe2Ge3WWbP6/WjtSStjvtVI5VIVn9p0HKHlXS02iGiRolqr3JNa0TTCd8y5muagWlpW7/ldNNynwCUXclMiT1xX4IEf/qjnx3/+KTf7T2h15O37xN/+40/5yU9/xrSeMc3ocJ/Ndd5kizPo5u/pF9BaJaYVGrim5p5UErkmnfvKNtRGjQQlpw3k7Hn5/IZx50BmUk6czjNDv+PZK0foHyGP9Nxw97tH/v0nv+f2Y6HSU+NKeUx0d5Zndafb7rZFY2wlz0lvhNZw2lnMTcBGiztXYvLMxfFxifxxzSxuwI1HhuOe3VVHs5XHu1vWKW8XmZWUZs4PD8Q5ctxf0V9dqawvGAZniN5oJnCd6E3PsdtxfX3F8Wok9AHfd7wUoVnDv/38t5w+PGJzY1oKxusXR4b+W2BxP+zxPnCXGveXhcfLiueBPN3ipODDATdU2t4SQ2OyhUUizTaun1/zxdUVvgukOHP3+IF3d+91XpYqow0cvvyMaS4UAikVvvnmdzw+DMxLxLYE0bCsC7nMFDF0GPoMh7ZjJwNrp/PZdTXkVeeupiWCCXhn6Tvte9vN3hrEbUbgyNj1mJJYs+aFa1PE42VVgZwJliqN4Ix+yVqi5QkjDSvgqjBatRBMqXK/LtyfLnzSQb/rGTvLOB7pvRC6jpQr5/OZ/RDo+pl+yARvqKXSSIDo0smiS7imi6AULSaDaRVnwDQPzrDOhmuE87IyR11j9V6V5HldSEYwXcdghZd+0J8vL/pqnvVWbKyjgXbsSUytUZtDWsFLRbyjs564LEzrQisFXxzGe5o1dPs9ne+gNpa0YE2miv02GVRrJCdtFBrxGColRaQVxtGDtZTSSGlRkl5rdL7HeyFl3VGUXLf/Z1sutSnfoLN6my1Ushh8q2oiAJ1X14ppTb1k4qimUSVrk1OeDktUjGm0Cl9K2eDvhSeFtjJb9AFWt4ucMUVvyPof2GatntasWh2qRsHUs6VxulZ0lu2NMLiAl++40KJqfW1e4f2l8nZtnKvewLqhclkm1vWOFzcrP/7h97jZvcAwcpot//Kzr/iHf/pX7u4fMPIk+eLbkK5s23/17+jCSheE29EpohbVkjZtr1KplP+4uc6rUqyExNj3vHxxhXOFWhLzolGZ3cESxpWaLCZec/v2wi/+6895+9sHUg6bk9ySW+XxFLm9faB0Ce8Cu+GaUitLjZhFye25GR66ghx7aoLpXLl1hgcCDxU8gYML+GCRsnB+PHH78Z4S4Xo84C26QPAdGUdnhLZOWN+zHzzeZpztcOY5LXW04mn07PbXHG86ht4y7EaMtRwOPUYS//7zlY/fnEhrZWgeKYXzGrG1MfQ9xg4sMSINnAlIgfV0zz4UjscB0wWkCQTBLJFSK6fTO1p7zvPrNxwOAzY4qB27zvLs+grnhLysuCrshxHrnIbGY+YvvjzycF54OD/w/v0jv/3NifN04iGeWZtnFzOfmsBzt+MmdMhoaQEe5pUqjRJniJGDswQf6PvA2Ds6Z5nOsy5aJernpCaokZodaspVy2gTy1oqNSZMCNgGNa64mrhyjRA8Yz9Sl6T5Tm+Z8sK0rriWuO5G9kE9TlO1rLHQ1QVyITiPcFZEZankNOAwuI1Wz7fBd4NpouJAvYNiN4K9dQ7jdJ7YSyDVSlSUCdK0VWmd02hbUM9as3vA8HGasV2gtx2tFEprpKJp/NIqpTScg84KuVZO80I/WCLCJa5QM65YbE6Evsd5T64CSfkHZWN8eGPovcEYvb3lrDPItuVCnVMsYq0K2y6tbBAlXQohSuhaYmaKWndtWEwzOCqYikGwypLBIuANXrXPNIP6tpohNdG4YrCkqlCZhlLwRCqNSKl6SJaqluJSK+4pq0uhoC2vWjWPryQbto1/1QiX99SiSiidF2uultawVkcFHujEEJow+O9of22lkDLczY73i+UxW0rTEe66LFxO9+y6yI9/+Amvn32BlQNLFL766o/89d/8V77+3R+U8G16xIiS+XOm1qKzC7SDDG3TRusrgP41W9a10ADvnEI3rMU5D6i2WQSs9bx4+YJnz3eqz6YRYwTT8MNKipHlruP267f8/r/9mvvfP9KKbjmlWVozVHG0YInMNBK1GUK3o4jQ5juWuPB4XskZSInHZElrZlkLa4FYNKISkuG8LNzeZrxZaVU1H6ML7F3h6jAi1XO2fot4NFrL7DrDftROfporXjqeX1XmteBd4XBwXF0FDvue4Dx1WdSD9T98xvPB8fP/9lsePy5bK0pbLNZ7um5HilvFNlauu5HPXjxH5g/seuHqxTWrKZTlxHJJ9C1wuB44HgeuO7gaDLbM1MVivOe4H+mTo/ce2aFFDpPI6cLoLKazuOsbXtfC/WXk9v7Cu9Mta1CMIVNBXMXvBMNCEDhe3dAfHM8LTHFlOY/IGggISSyljojxpDwRS9GZm1dwckyVWq2Gx7fNcIxJly+lkDNYKbQt03joHNfe4LuOhOH+smKsoVqPc4ajsewDXIkuTy8F3j0+0srK8zFwCIauE6RGXWwUR5zQV+jeIFZ1KGIEimidebOFtloQqTgX6LywpMKaZnISxs5jenhImWmOdM7jfGBNkdoSPniQEWMF6RwmBGWsZrWWylMAvukopWTFgUrNrKUyVSGWokvQp1fmWskpEQViAVvU7lClYXPDukatK0306Gut0igbb9VQqUzLpMRAMd9m11PeFtW5sMTGkpoe3tYhTTXbgYRUvSG3zSIrrWG8YRCD2/CeqQnLunCao6YuCMoAMFppslbjBGof0DFhfarDi/s271pq1QsEgmk6K66tUVrbXvm3emuKWvGlbEkC1cnUtmnCW8Ohc+IgFV+/48y1SeOSLB+j41wNGUvNjXk9c3//Edsmvv/5DZ+/+ZTgjuRqePv2kb/9h5/wLz/9GfMyY8SqZVH0mV63aqsYhSKIKKRhi/giRn/ReZs9QcO5DRgsZntKFl2OGcHZwGG85vNPX3NzM2Jt/dbX042wTgstHji9j/z+l7/l4Zs7bAuIN1tDxJGd4K+FNz/c0d+cOc26+FlyxQ8jzYq+jrSGaZYSPR8/nEkXAdsBghdLPw6IGC7zmXVeeLbveHV9xJLJ80I5RwpRITNxhlTo+w7vg0ZniuFwuMIVVUO7UBh2O16+es4Xr0f6vmHbwnp/T1kSzo/cSOX71z3999/wcTxzd3vWCqR4sAGawdbKceipVTDSsav6yhzMjhB2uJCorbDEQqDyYhe42gdcOpHO71hr5TQX+v2OZy9eKNZvvWisxRk+PnzD6XTLm5efchyfaezGGozf8eaz57x8+45UYWiJ+LhydAMH17A1YSkYmbGlsQ+RFBNr8zjXAwtj5/Ei2qSpUXmfFkQ6UrGk3LSMUhOSM84YOi+02vASEHTpk2pDnGNsVUsJXeB+SeSWSRFocNyP7ELHlW/0xlCNzjHHPiO1cNhpfthQiWvWz6R1FCNgdFnmjb6W6gZ2q4AW3Xs4Z0lbf90Z1cIYyXTO4kXwjc0nBSKOBQ3Q+1SoprCWwt0SlRsQEymrBbbKJufcolfSEilNNKMOtFQqLIXd/sDh6jnLfKEkNaDW0ogpYWgb6s/hrMc4PSghfoviM1KhJYyz22VJTQHWWH2rbKLFiZy4zCsVoTQFbjfTFCOI6O3caJqhbHnWzlg8K5mqMUurOfbRO469IXhLOa3MRb/3XQiI0YxtJW2OPbOZobYMKmoyqa1Rq/k2H4uwabn/JE+ERm2FXNZv56hP7dHWEqXWDZRu6UUYrSG0hqvfETk4J7hdDKdsWaowXyLT5cz5fEtK93z+Sc8PvnjFYXxGo+P+ceGff/JL/umff8r946NCZksjSVEjo2xjgW0cYIxsdPA/ARnM1sowVoMTremtVT+oCog2Yui6jhA6rO25Pt7w+vVLxrFDJJNqZMmPLPVMaQf2458xfh7ZJcMH/w0f339gjjM1W1o1HF/0fP6XN7z+q5Fv5v/k/HHCNsNXv/qaw4trCllzltaSoyUukZQCWZTys+86ng0ju+BoLfHh3UJNgTc3R652gbIq2d1jIM5UKl4aPgjHXU/fD6RvX8kWWp4JttINO/bPXvHpm2t2PjLdvSfNM3lpSFMxH11jtI2XN3taTLSyYk6wJn3VarVArdQIO2fxxmC2pcuwu0KMR0pkkMB+59mHynHsuN4NUFceP/yOb97d8/tvHvjRX/yYAMTziZZO+M4Q9h6WO0gTrSRdeojOqGyL/OiLK4bd/8z7D78h3b1neHXFkB1lvjAtF+YMUrMC4I1hulTe357Zh47DmBk7RzBFbxmx0kqkimB9oBRDeioGiMFbhZjsnCM0waXG/Zq4ny4k6zj2Iz2qQkmrakXEeta44luhlorHMQRHF5Qnuvewf76HFgkY1inybjozesOuGEptNFasbbjgwW/a5y0n6b2jC4EU06YsMuRSsRtD9KoPJNG9ccFr+F0ql9aYqCQxLKUy10I1jVQjB+uwNhAb4My26U54Kzrr9ZVhP/D8+RU3N9d8+HDL3e0df/Hnn/O9Lz/nmz/+gffvPjCdFuKsb3jOakieXGhG9MEsnpw6MAbrDd7pOK+0rPhDI1izkaiKHs4p6+t4zIohNc4qD2FTthgrODGARSTQ2hbNlEpvVY9UmmWJjfv5go3C8dhzc7VnqR1lWqkGnLfUlvQwNE7NyRWq6BtMKU+UPEGq2WaxFesMIYRtjKHacCXLau72KX/rNlPt03hAjMGLoRfDYCyDEWzVW+x3Olzvzo3bWTinwukSuX1/y7u3f4B24YtPB3705efcHG+AgXlt/Pdf/J6/+fuf8Juv/0AuCnSopVKlamRK5Nt5jdJrtIZWK1QM1tht2dWwYsCJ+oCoCtLdyFIYxaHpTbJx2A+8eH5FFxy1zTxM3/DN3ddc5srYfcngX2HGxMtXzxj+pzN//PV/8B+//BWPt5nD9RXf+y8veP7DkYs98ftfKfi7tMZlieT7R+0sJ0e6ZJZTxZsD/eg4t5WSImmNLE2QZIDC6HvGoad3jd5A2O2x+x3Hca9E+OnCPC3M06Kk+Kofzvv1zDAKgmZFn714xs2rG7xdudzdcrl/T5oWKJ5adjzez1RXtqbRjuAd+7FDsMQMxvaUpBvqhpLZESgt0+929McdS7xsqYFKbzuwjrIW5scTlYU1z5wehCEcme5m/uPx5wQi11cd/XhDH0bG3cDzayVQ2ZpxdqC0RCDy6uB4+ewLPrze89XP/oX84YHDzhNrR4yzjmzOCWMM++cHjB9J5QPrGhkFkhP6fVAojRXwGspfSgTxGKelLKU2KdRYtpqnCDyUyKWtzMuibCavgJKCmnhHPEZmfG3ENXKKC7v9wOh3jNK05OEaDQsFLrHxMCXs4CkpUyUhtrLvd4gxm3m0EkulYXA+4G23zfRPnFchFUNnHaYYerFkm0kGchVlzAa9gORaQSw1FrIVxBt2XcfxeIN3A9OSsbbndJ74+PGjKrsDvPnyDV9+8ZLvfe8Lht2O3/zqN/zhd7/lRz94yeefPuOL1wfO5y95uDvx9t07bu/vyDXrzXPN9M1isDqi82rSLa5iB32ApVwxWUGzznhSVtBLbUqJamIQZ2gxbU0xvVWzmSyMEaxxOKuK61Rnasv0Q9BEEZZzLCTnqLXhbSMEOOxHknEUNNsaY6VkfSiIGJqYLQGhWWhxRmHXEUR0HBV6HbeURR8ktIqw6aJQkemG9dY/t2iXEaEzhp1zjMbgNiB5577jQut+9UzVMC0rp8cz799+w/3dBz79xPNn33vG65fPCH5Pyo6v/3DH//Z3P+Hf/v2XTLMqmmUjyLTa/iQaMwYRR0VvDqWo59xY3drVumlwrW6Ika0lUfX2okN+hUms60wymX5nubrpEfbVdkQAACAASURBVFM4Te/53btf8OH2kZRfc+ivwOhmse8Cn70+8uc/+gF/9V+uebirHJ9dE64LD+XM9PFCOlckWcQHsJ5WLWkyXG4jl9tKK57H6czD4wUXPCLCw4db5taUuL/RqKQ3MFfA0ryhP3r2zwy2DhyWQJwztx/vdUaUJ2gZawqdG9nt9hyP1wzXHcFGSjzT0spxN7I2OJ9UNof1GjfKiXk5UbLe2kzXuI8T83TGoXEjcYbFQqoJMYkaOh7nB+I0KQ4vZ9a8kBfH4wPsdpb9wfHu7pE/fhRK9tx/nDHtws3esRs+xZmOmgZKynSDp8SVwgO+C1jpcN3mVOpvqNKzC7/mIg8Yu9If4OBGlrKQ5qqRmCb0wfHmxQG7guTCuhqGobLrLdZ0ZJ80tD5nrNfXwHVR7qfIU5uv29iiieoqxWlR55QSawa7LtoT73c0BO8CnkYrK7k1YlPyfTMVF9QCof14w6V2TCkzGOG86CzceCB4sujtjAppXalNMFKxRFLJPJ4L9wsk17MPjq4IizTeS+SxFGpWKV81jslANKIXCeNJVVGG/bhjPOwIwXG47mgEYp745PUN14cdzw4Df/k//oBnN6OmRqzjR3/2GV98esXQOXpfGIPlMIw8vwm8ej3wH7+2vLs9UapghoZJlZb0ge/8djMGggXTCbZ3hGb10GqOXCspL5SqsU3rO4LZAPgpb21CoxGpnDUS5hy97xhHSzUBmlp4odKk4vuggPhSsG1iauBjotsEhU2ELHpIt1ZpreFdwBgHNSF507rkjK0Nb6GIcgeoympgmy/r6FLhN7XqZ0g2souxFiuWYKATSydmW0omjBWC/Y5sgY8TTK0wLwun+ztyXnnxbOQvfnTDD773mv1wRamB97czf/23P+Ef/+lfuL2/p9aIsU8XbZ0H6Shg+/sn62LOGxJO1LPUtptq0aeHzmirNl3qk6bB6U23REorjIPw8uUVV9cDMZ/54/v/5A9v3/Hxw0irgd5HbeQ4y2AbwWUGb/jsy2vefNGQYJhyYno0BEaeja+Y95ZTglKENAvz3cryWLGtZ1oSjw8Td/d3DEPg5e7AOIyqe2iwXiLBVRqBlrVHf7zqcCKsl3ukCkJg3HuaeHLMxKkSE4z7Z7z+7CWhrzivdtD1pHO8oevY9UfW0IONrO8r50sm1oQLnrQmgmk4UyltZddpt1qa0HUWnH7QDA2ksqQTcV7Is/JP11KwGGoNHMZAL5bSDHP0fHh30QhKtew7T1wND3cXht0ZsZl5OfHpp0e8UwFddZO+4rqms3Ei+0F49eKG373/DTl+1EZVv+f62BNsg1KpEpFWcb0jt55ZhGIyc0xYbwi+Ya2lSiYEwCm+LgTBlYrzgVqFanpyXvES6aXQuY5sHRnLvMzUJUJt9MXQBc9g9U3FGEMzhtwsa2rUrRGUcwQbKOJ4rIZzclo6SIahWEJnN/pSw4cdcXUkDDk3TAXTykb873j7uPLN/MD1YeBq6LlPC1+niWWbjUvVFloyYILTTbgUChVKI88La/qAs0+h9p7SMt/74Rt+/IPPuNrt2fc93kNtibv7O1XcW1EUZls3HWrBuoJ1q86LseRmtZShSC/VyHtIBaiNXBMJw6HzHDslla1rIgv45uiM27KiGvY3gk4+xeC2unYsULIWGxgL426P7wa1HTStjzcq3sDOCFIapXpytjgb6Xyh2kpqCVMMFC2SOGsIvtM5awZSxJaCo+KDQlqWUslFD3vf9OdTcJCmOUpTOIsVwYrbYlpoHNAa9mLwYrfvkeCNwVv5vz07/z8P13enmSJwmU6kdGG3d3z5+sif/+glV7sjrXU8njP/x7/8kr/9+3/mj2/fkUvUvF8R6tOr2gbFzRsn4KkXL2KBvIFpM1KVH9Ba3frF9U8ZWLZGF2z5O4PUQj/2vPrkOePoWOMtb99/w7u3kQ/vr3A+czgs7Hcj1hs6V3FWn65GnArJjB7sZa5w9vxg+JL+Wc8v7m55mDKnc0bmgCRHM0Jcle5DidQpY13Pzntc6LDek+dIyIXOOCQnSouYZmipsOaVVgzSAnXsCD5T06yAGzdyePaCV5++oLX3TA/fUKaZ4AfEDWSB5DtMGKlSeH93z+2tgiqc03xs2Fm6nWd8vuNVv9eq8uVCqhnbB1gjy3RGHNhasbKypML9aabURu8cnXj6w8jhZkSoWFe5PgyssxpgAXLMPNw9EoLw/OULrgbD+nhHaonbVTD9Hf3Vnm48MpQDXZ23bHLB+g5SrzM4SdQyIXXFZPCmowsdUxbet8p9gaveUSmssWBQA0BrCTGFKgXBK0Xf6xy+NYsYR8qF82rwxdGFnruYucxnJNfNGixbH79u1VudUZsK85pxwVGcU4RjzCTJFBpzaqTUGHunJYckSLPUXMhSqCWTU6O1jtyA3HC1kqQSMdzVyn883ML5xNVupFjDyTaaFRy65NXR2RPFamOtblXvWioxL4AKF7tO850f7/7I/fOO6+NIsapX//0f3/HVr39JihPPr3b88PPPeX48YkGbl+gNrORGrZ6GI6OHD7ZRbCWaSmxWg2QbbWnfWcbDiK2N86MuR7thoN/ZDVYDOUMIlmlSlKZsjUrv/Bbn3ywB1hCc00VW07RDFdW+WMkgiYJlEU9wEVcMRQK1GiiCFMHbzGAThkypnrHrcKFncJnRFVptxAxrzkzLypJ1bOOaAvGdcfrGUAVvA0502fUnP1+jBwax+KbG2M4Hhm6TQn6XwzVtHd11OePsxLOD48c/uubVi+c4e2BahJ//8mv+l//1H/jqP3/NumqMCdEwN6JMxrr9QCIKGjGiB+vTmKC1RnnaujXlDmghwjx11rDO45zHGI8RnX81C8fDDW8+ec3QexDHfveclhM5dnRDT21K2rHStu3sRuQSEBQQzSqk+0ZYAqNk7opQT4nbPzwQ3BWjtUzTzOP0QEyJmmZ6qQzN0JdGrw9GDTs7qzXEYHF0WIHDoePl8x05ztzfPkJJ1EtDgtCiwXUD4+E5x5dXSGcx2eFBg/e5YXpDbE/+n8w0L4TgORw9d3e3kAvGOcR0DOOO/c0VZtiTqrC/HrmsM9mAqT13H2Y+PNwr1Ug8crXD9B1LzHTGcv3imk8+uWEcDO/evuX+/S29eY7xhZpW8LtvYzjnyx3BF26Oz1jihTRP/OGbC7eXyqsvP+PT73uurj3jQWgUPr67I84VkwOprrSQ6PqM34y1RnqqwOQyb9cTb+8KhhvEF2rQEkbnPLkaJXA1VXp3wWBEb+00NPjtGtX2jBSGaaFbzuScsMZjcVixm+++krPGtowRXLPMm3zPG0cUx10Rzmsm5QsNSxBPtgpStt5RSiOmqkYMUTuFFOWuppQZQ4cbPLhK8VpGSVaBSE48ral9tAqbYkRjVU2qqo+egEWyRc7aU/RKl0EuVGKa+O3vvybnyrNnL1hS5ddf/47bxwu2RZxtPE4nhi7QG0uwFTEKtsnbzL/SqKK2gJwTUgtrSljrGIMliadYiw2Ovrf0ptL7nnQ9qLDPKRWrtkxOE3FyfHxbeP9+Iq516+crJlBMxdiGcxXn1XBAM5Tmscbwf3L2ps12XNmZ3rPXnjLznHMnEADJKlap1HaE/f8/Wt1uyZK6LblLUneUqos1icUJ08W990w57MkfVh6AlhweyAgGwABxARxkrr32Wu/7vNFZxGRaK6RqsG0heEGqpRiQFgi5IlIJXiVkZMi14jvPTbREY2ipUTJ0zlNyxteKrQlnVHXgncJYCgaMxVvBG0Nr6hQU57Ct4RsMRjkIxlR6D713+B8b82KsYzntMWViNxh+8fM7fvLZJ4RwzVIGvvr+Pf/xr/+RX/36N0zTpEYHmiZMGhXm1g82Me1W7Yr6AqXSNLXNrC4K80ExoCzGFWazSrZa0xhrfdAMzvU8f/Ypnz7/hOAEcR0vnv+E7WbmIQp936vrAuVTi2QFwBoF4hqjKQFtasjZsMmVw3Tk8f6J6d0ZPzni4KntxLicOZzm1ZkBXQxsTcdtDAQBcVZxaTUx9JbdVaAPHTUfccESw0D0gTJX8rToTBDL1CrOG549D2yuoNUFMR7f3TIMwjJNzHPCFEe0hiWNLOOJq03Hs7sNn9wY0jIjTmdWpotIDPjY0YXI1hpuBVKr7M9Hkq1MJfN0PLGQ2PSNl5+84DzDXTfw7z695fMrr7CNBm+/mTg+HAgyklLmcT9z8/yOKD3NQm0dJVmW88Rpf6CUxKYbmPYzf/jvf6DbRp5/+gKxlnev90yPM1ujaaO+VboQsUMg28ZYvYZViuFkMvvTzIGJuFOpW25utXIbQheQUjEWBLVF16IRO9YHmlHVwucRMMJA5mH2TM0y10YIHUaUyjaXihWLXwPosIa5ZQ7nhdl27JMwLpklLcQQCb6R20LCMhXFTEkudEEXH6RGTYlsEy00Wg/SR07jwtvjiYqlM1atumJwGEzVjbWxjkbVLr3pOO2i38QocKXWhLeWSiW3GWcMiGNcEl9//x2v3z8wL5l5WRCE4APDsMEFS5METl1HtVx05kJtM0ur1OZYUmOaJs7nPfOcuLvZsYm9gl9oOAPRNaKreGvBRppVC7tpTkX8xmAWoaWzjnpEaKWqy0l09im2IHZBXFnNPB4xGkjqbQVXdDhYwGZBrEe8x9DwrWLiCrUWh8kZMZUO4SpannWFsiy8OiQOx5neeaIVTdD1ns7qc6RZWYm6phZo/74SzJzotxhc1QIeVixBEDVp/D/ss/5fpFinkZZGOjfzxedX/NnPPmPor6m14+37if/0d7/il//11+yPJxpNl08rRdysUbqsc9faitKu7GVpVVfNa12tafohiWhH2+qagbOKfC/5N1SDlQZO6Fzg+d0NN9ebFeawCoprZbe7Yrvt6aInhkAImhCgmtr6obhSGqSGSYUyz+yfnhjfn7HHyq4FxtOJ/TLzdFpoeG6GyLWdOc0zHeBb1Q+xqnKh72DoGp1vWMpqfczsDxM31z3PP33Gcj5RlsycK711hM4jzFg8ZKfkIr9jc+WJ3cic1byQ84HxdGAez7RyxtkznQffDYSrO7p+YOgC282WYXdLNwzgZOV7Fm7PG7YxctNf8XB4xNhCHHqwN5hwxbPdNTsnnPYPbFzHT3765wRj+Jcvf8/hwXOahO9Omf3bA+6+cbN1fPEiMN+ciG7hnDU2I3SK/punypwK0/yaVBuHp0SbZXUPeVwpyjl1QUX/p4XDkhgJRDp2gwcR5pTpkmPKYB30mw4XHG1OGJvworpTTIJgMQGCD8RWCaUi2dO1HW/GzFePR+bWaFLw3lKtJ9UFW6GVBM4iXSTnmaU2xtyYspKarFOeU22G82K4PzeWZIim0jvVz3be41e6k98EXO+xLnKm4+unE69PMw0lolWpVGlIVUmQrO8BTUMITRMtOiKUuqpqVtdiawXvLCEKzgaagdwqplXKPLEsCSeO2HVsomMTI1KtFkdvNf66BqyzGJkQZ2h53ZSbSk4z8zQRQmTTBZzzdC4QXaETixOwRqVWWEOzQLHr9r2COI7nmXms601B7drGNCYKWSriDOIT4huGHjGO1makFsQJeM3Lq0WIqUOc10TjolBvCdDEUbFIFXpJXEfPs87Tm4nZbpmvDWIP+JpXq71m3jmzLqKKgmIa2sw50/BGdwVeUBBRbTgxRKuYRYPBmYK0jJMfORaYxz22HPjkE8+f/+wlV9sbjBk4nOCX//Qb/te/+hu+/uZrlpRW98ZKIFoLa2urtm29+td2WU6trEZz6WTbKgJWHawyHxXWcJFItFY/zF4RFRoH77i73bLd6OC/1so4nsgFYh/xwawOLvCu4WzDUGmip3UzjVoKpWZymThOB16/ecX0cGTbtEj98f2eh/cjc4Y+enwz9Dbiui1tKSx5woZOfdw1MwRDJ4KkmdoqJY9kB0dpODex3TpsVFvl6TSSUo+4xunxgGsVnMd1AYcD45nykZQLzg80BJGevgfjDNSZCpQYWULH3bPnPL/esul7rA+Y1XftciOXTDdEOnvH1gemjSBtxHc9NW6w/YaaC+/ePvD7r77n7uqKzz/ZYOPM1Y3D14GuL5zCwnf7xvmp8PBw5nQovH++YzeAyZY8A+cnkJFktjAF8sNEAh6e9twOgX7NFTO1cs6QQ8FNlfN+4c3TTI4WawZ8XBhrw+AIVe2anViiCYjtiZ3DSiN66DrRzta6tSFJtJLI44LNMIhhENh4x9QyaTxRs6OI0z2AGOZ5oWYVrzcbmKUyLgrFblWDAVvNZHEsxvJ+TqQi7Jy6/HKqtCaU6DG2IsaR8Lw6Lvzx7YH//urAuVrE6PEuRj40IUYMrFHWxlS81WBD3Q+oltRQaCuoSHlNdb0E/l9zpVjZqUMX2fUDmz6y66MWfnF4CSttzmIu4n4rmJRVPpkXxFQ2wyop7AKIVW5wqMRglM9tlKNcrZqEjOify2Bo1bGcJ+picG2NuRYwrbEsE9lUxPZ0PhCDyg+NqTgSplqqC2ALjkaWgA8DIVp8U/azGEOpkEVwTdhEuPWV571jK42aGs5FrBeuh6x5arlwOhV8y+SsUd7FQlVeF9RKsEaLa9Mi61gZFCJ42/QGjGb2eWcQ+yNNBG154mrX+POfveDZ3XOs3TLOnl9/+RX/y7//a373+z8yTufLLX8tgmtewKpnxVwGnJoMoC4QRYoZ0RdB+QKqm6trcqMxK8vxQ3FdlQZGZ1FiDLvNhpcvnjH0DjGahzOeZlKu+MFgRLO2aAlnVLivCDH9dTGag4U3FJt5nB84p5HgPMtSOZ0PtNzwuRFdR3SR82khLI0rF7G+6AvQoOaCyUUh38HTd5HlPDKXjAsGa2ZyShwOVR/CKpynwrif2b8fub0TJJ0IfaSVa2roaKVpZ5Uqx8NCsJWr3TXDzTOmlDjuz3i8jmEyxOjphkjoPaUWSlY2Z8sLaZlWZ4tlFzN+HnFtIeNp7YzMI+Nh4vzqiXd/uueducf/Ty/57FNPv7mi5YlAoF73PPDIw5hYzvDmceb+OGvaLSMvbjs23ZY5J6Y8suSRsTSy13iRGxvAqlC+mIXvxiPH44E72VBr4JwNRQK5GZot7JczqRk2neeqt6S2MC6Nui+YOjL0M9F7umHDnEWtzLWxTDNpmcmpkbOBqkkFg1hmqRyS0veLUaeVBIdUT0qZp3ECa0kN5qwGmGoyS16I1inXtihjt5aKVINT2xgO9em75kmj8Kd3T/zz67e8esqMRKrRGGu1qhakriyNZtb3hTUY78Ixzavj6OJ/V+ODtw7rCl5UFaJ5VwZvLV0IDH3H0HVs+54h9oqHDIbgZP1ahmrWjthoh1aDp06VWgyx27Bzhi5abFgtp0ZdZd43xFa8WDAe45xmBEhRlU815ATzWJCqjFdsw4qB2git4qhsvGXoIzGK3thkUXQgDuN7rGmY4jjjMSbgbCM41vTZpstC44nAM594PjR2bsHWFTDfGq2NOJ8pppEFanbUZEgIy6Kz7taylkIjODS9ovMq56NWvHN00dN1AVMzZcl00eNcQ/yPNBFsY+LPf/4pn738FOd25Drwx6/e8u//49/xq19/yTRP/BDJ9aEAil2jXNrHgts+bv+d6EbXriOA3Ax6cKgMRP28H6o1pV5+DcCKntIVhq7n2e3VGrncyGXidCqU5umCJ/Y9Ye1wvSQ86F92E+qFpGOgGwa2dwPnb04sbWbYXJPqzPv3B/rq+OK6Z6nCDJQizMcTixUG7/HiGXD0fYc3jU2s9LHRdTPeJkLv2ewiPjYKCvpozSJ2w+1Nh6Qz58MDp8czoQXabkdrDhkch/GEs40u3DBOe+aSMMHRb685vr7nuD/RRc92t2N3E9n6GXETxgm2QE0T8/lASRMVNV7kmqnpjMjE3IR3pwmXG59sAnbeI+cH2nkCG6ipUebIzc0Lrq9HvDheNoffeXb+Dft3M/NomZcMTZhTxBphGSxzXqimUI3jsBRSa3zy8hm2s+R6QoJlsY6nKfHdac99LfTtjqcZ6jIy0TimylKzjpKk4YIjLROlFI7zRJ2PSLV03urBXTWssubKeE6cThM0y5IaSxFcC/QIs1hGZ7X4NoMLgaVVmhVqcyRN4KSJgG14LzgM43lmqWqKSecT1QoiOjfvEHzQhIhDUq7T/jjyq3f3vDrNpOy0c1u1sLloKoHxertq1A+7BWsVFn3ZR4gp1JYUJKLXOUAxfp33eGeJwbPpO3Z9T99FuhiI3tHFQO/DmhVmNI1YSwop15XEVXDOkstKrFvpUY26WlZ1HikCwS7E6AgenNFEV+sEoanzbJU35TmR56KWWdsIXcBayzxPSIsgsOk8zjmCDTjv1gNHmynnG9KEKsoeNiJYm/BeaFVvG9ZCL567AC+GxFXUnUVJgrWBlpUdbaVoioaxzALONc3uQyhFR5DWwDrbWJejgl3t+N4pq7fvLGUpmLKmFdiMcz9S5/qLn7/kpz/5jNhdU2vPt68O/G9/+w/8H7/8bzwdTpRWPgh4YS1+fFQCrLXxw3b+EpNgVinMZVzwsQBfwnHMD+ay2unaNS+LVjVe1xo2m4Hrmx3WWgwT87JwGhvNePrYsRu2ODH40PCip2UzykwQjFKUmqHvPZ98dsPuu4G3bzSWuIuG294ymITYyCnBvhqyGHxqtFI4l7KiCSObEPCu0flC8BmRiXjtcP0dVUZKOhBsANNxOGWc77l51uPKmc5a0mxpxZCSYRkb7+/f8njYM/SeP/usx3sdnyxpIT0dmE8zlIm8HMhpplWh1R7lV86YZjntD5ye3mHqRB87OifUMkKdyCbz5lz48tXI7d0n7K62VGtpFmIUvIXDu/fYpfLi02fcPL9iu91SW+GTl9f8jz+94/W3D/z293tNYU2Vw6HQpHFsCneupepLEz227wndFcfTI/X8iNtB3hhOrXDwwsNYiMtCRePHrXN0rscGS5qOnOaRDcpMNR6odV16BqaTIeUJ7+3q0nPQAqZVxnHW4D/rMc3gjcGYxpw1dM5YT24qdi+LGlrquirovKfvAkbgdNxTqm68vdOtfmpCbgp9XnKhmMLDMfGmqe12P8+8mzNZL5fKvBCNxNb34LLIbas6RjXISipVp6JGxaCzzKbDgYupxpmOq80VV7c7Nn1gE4Mygb1GfAcvRG9X3aasPAMwos7IUhsplXWWuyboLtoAYFb7efUK2BbBSsH5Su8N3uqHJNKwVhfYIgoxKYtR80AtKyhdiH2kYgh1xjVDs4au1+If7ao0cH4lW7U1iUB17V0DJ1VHF9VhKDgDvRFuvOXlULjrG2Iqc1lNSk0wRQ8gcIhxSBWGvgMWyuKxWOalKoypXXCEettNecJHS7BOFSA1sSwaIGmtrOPNsu6VfkRx/fnPfspud4PIwP37zH/+u1/xl3/993z/6i0pJ4UasFK+aetmn/VD/+jK0MLZ1umAFlbzYSarp3a9WFsxuvldO189oVZEYa1rAq12vbvdht1ugwjUmpimxHlcE0jFrwPyTEsz5IoJQpNCs0q9UjqOQaQSrwKfffE5D98dOL0+wDzzYtuz+Mxp1nhjyRMDRqMejJBbowXP0jLTctB0hFLpo6eLW8Iw0F0NFDGUpWDtgPgbTDzhpOHcka47w+I5ccOxNNLR8vTuPd+9fofzjpd3W16314SuYW1lPieEiMmVTecI0RCDEKwliMchtNKYzhPffP0t4/mRm43Bt4L0G/w6bkmtcTw/MaVC854JITvPWRrWKU/14XXBJeHZ3ZbqbnGba5xtbMyO7VXH1cbTdx3H/ZnjceL+faSaHaksvDuMHJaC6XqmZWI+nEjTK4Z85KWMHFqjICwCi1gWa8m2YXzF5cxdHLi5esZxPvP2zVG5Es6wCY4uRvBCSRXnFVaiahDtvhpZ0XDOsogllbbSlwSJKn3KrOnBTcdJuWlhXbmYtFaYlhFX9GU/nU7qAvIRqs5gre80OFMMdI65T+xz4m1qPF686+LWzlN/X6ZVxAS8t4hTCVJbKU6gVP0mGilPMzSE1nSOylpoc86INDZhw8u7F9x8ckXwGkIYRHCux3shePBWi7jFqNFBGhh9b9MCy1zJC6QFTufENCacF11WGVZAkhKyotcrs0fztLCG1tRmzKoAaFV5qWnJNDLOQhLh7fFEM8KtX8cKURh6YQjaSdZLXVgNRyLq2pSKxrlYA97hsBhbCDRuneFFb3k2VHqBVCMZMLmxUMDKij4EU9eo8BBxMnNIiWyKLqwkkLNZAduGVtoHIE0IAgRyqowp42Q9WKumo5TyI4vr5nqHsZHjqfFP/+33/NVf/z3ffvtKiVWXgDFWLcIq/medal4GscpvVddV+3DyNkXVmTVOt15872Ytth+72cviq31Ai+sD5r3j+uaK7abDmkotmfN5Zp4WaBtME2rRk7XlqiNdVA9ZmtNntervuQoEET579ow3V1dM377HlqqpB021sFYau2CwtVKwjK0y0jAWrmLDuIz3+vVrbYh0xLhVCrsIXX+F+Cuq9ZR6QPKE1EIMkdZF3p4Lv/n+DaG/ZskFaqDOldN+IY8zIomhs9zurvChYKph0/VEZ9kMPbvhiugCFlmXA4bYRd0Ie9QNZnuq9dAanc/89NM7/O1ANZ53j28wOfM0ZVJe8AK7fsu2a8zTPdOpJ296bOww3Ya4DfTHE1IeqNPMxl+RN5bjKWHLIy6dOI+Vh+lIrZUrAVdGNh42wROCoUavLAIqzbs1NLVQ0U2smUbm00G5m4g+M1YtkjE6TNfj/co+Xf/cadabTc4zuc2Ia5gKRTRee2qVbHXLGX3AiKWgJhbw6F5Ji1BZ40tK1bGWEbfuC7RwK33fkKgcy5lTzryfFvYJCpZq1gTUIhSTftBMoIVDjAKe9WmHdXbqrAUjquOtTbmnRvWvdTXhOLF0XWCz3dD3vYZWoumkloZ3nuD0uTWmaHCfEYyUdWksCltJhpw1FWF/3Qlb3AAAIABJREFUmFlSVYyf1U29905Tix1EK2xCIDqD8xbEr2CluuKtq17rW9PY7ZoRCSzG8s3TgS4KOy9YVDblfSH4ovAmI7qAXRUTRgxiqxqLnChbwYnqWauwtY2fxsyLTcN7gy0RMQN1SRQzUl2mWkc1lprVoGSMZ5oy0zkxns4s5SLrlFVOr/XAXaSg4nGuX5GWCzSLkaDUvjTruEh+5FjA+sh5svzqy9f8xV/9Hb/53e+YlomGpk+KceuM5qJDvcBZ2od/a9XF0sVRZdaEylrNh662rISsH85ttTKvrfp6T9NwMgcihM7z7O6aofcqwK6Z83hmvz9xXuBqO9MPW7yzqxtLRwpUQSdEdYVIaIdgmiWNC3M6EETz5s8lg92Sa8NZ4WoXaMvCnCvLPDGdTgxDpBs8XjRPxzpVKoQuIq6tdsNCsx6xntomatLC2prBup7hdsNnV45DyNw/wniY8UWI0VOJTEkTPes849qZuDHY1tH5rQ76xWuS7jRjugBiKCI8e/kc8oDJM9Z4pOuxVq+XpVZ6k4im8s3DW/LpSKgbjofAMnuGKAybDcNQMPlAev8tY5sxu+f4q+fMufD4Zs90PFDmRKoWYwbEHqjzketBeB4Cjw9nzqeZTSfcDD2fbQI3vWOzjUw9mH1Bim5hKwXTGWKMmDlz2j9wOO61c7KKocs1a8FDc+qtU1G3GIvBUwrMY2aeKrWu80vfmOfEU6q8XzLH2vA+YCwsOVNM1Q5nfTaoab2GV73OY1b2AevVvJEpHOcTxVvONdGmM2axjFVYmoAtVCqtfdyka0CfXYv3xaW4Pusf/TKr2qGt0KK2drdWHY8VxFicVTrVYTyS1rhoaERb2fYdXRREOg3RxHzQmWOKZtAZizVZr8HNcjon5ilr8GfVnYbzHmfDR426MfTBEULDWQcmqJrByiq1bFg8Ugykqtpk63nKwrsKn1uHD46Yq0rqohCiQ1wHRhsCkfXPbxqGTKlCcaoQCk7VQ940XsTGZ4NhGyE1oVnBNUNIUL0yTaiObKDYBVuFaUzsn048PRwZx5lSq1rxrf/wd2yNXReEgjGRhqeUTCnqiBNRzCNWsNHh+x9ZXMvi+eMf7/mL//C/8/f/8A88ne6huA+dpTEauX2JqBX5WBxbq9TKR3J5u8iqzGos0KeqFLXEil0JNivkhctGc23VL8VXjOaqx+i5udKUUWmFWmfO54mnp5HTWLm9ObO7vkY6T7CCd0l92iutXox2zgZYcuObd3v+6Z//xDevT/Q1kItCPmoLtFbpgyGK0c2oMZznzBZHyOr5NlbAZCqFVCZKG6mmgFnwYhQ2YxZqPiO0D1Sg2iyEjue9Z3f3Gb/76sSv9mfA4Lotc25QYNdHrDkyZSHPFpONRkLnhWSEfnLY6UxnKvFKO/1WE1KT+sWdozrB+w6RiJsN7fyGtBzYjyfyqbKbCzYbSrGcz8JJZq6uCte9J9YTy9NCHUfCeWFpkI4TwQl1Y5Fs6cLA7edb0n5DSpkbBDPc8+od3HrHZ7vIi9jotg23hWL1kK6uQ3NXK7a3kBu+wLDrmUym5Ey0FucFWcMOxRqc9ytrInFJ9qwN8lwoc6WIUG2gGYtIIdiMN+ApisGzQlolftFHWjbYplrt3HS8VIrqssU6asmUWsiGD7HL51wwrRC8YJunrF0QrVJXKn5Z0zassThvcZ1gPryTDSMrH7XpvLWuv6cPo7SVuaHJx6i7zjXGNPPdmzeAaKfYElebwPNnd4Tg6DtHEL+OA/T9EqNz69aUzl+NHjDjOFJbVRxga1AtMfR439EoWFHkYPQKQbEGmtj12mC08UFo1dPmhF0MvYscBfZzY0JDD3sXiFQkGLq+w/mIrKmtl7Gh+UC9s9SsCSVilI8QbeUqGD7fCbed3mbbCiOvuSC2AA5rHL44GiPNZOZz5vFh4elxISVd1DmrWEld3uk+yInFNM07m5ekNljDKj0rNNIq71Swt/2xVKzXr8785V/9A//57/4L9+/fUEvBySqv+rC+X6/+XE7bizRhVQhctgNcZrDyg65WC7FdWa8X3oBqXj9q9y6F/FJwrbVsN1tub7aEFei7pJHTKZGLUw2fGCpZAwBtI3pNUdXfymp4QKnpX/7LK/7mH7/ky9+/wsyGT/sNYdfwJ8WaORYGa2iLOlBsETbVItIpqbh5fBA6J2x6Q/BQZVa/+Pr8+U6jo03TmBBnHKHrKViKdARb2W565meOV7cHHvcz1QZOpyddmjlPbo0+bhEZwBSmOtPGRH00bDY9c9GrSt8MrhVC3hPSAUfBs8XEnmA0RNAU6Fi4CYVrX3ibR07LhBEVkc/J0UhY6/Eh0G28+ubnJ+ZpZjEg1XCzec7ozxTZ0UIkDgGz8Ros5+DnP79lTBaTEv70gDk9kF2lhqymEhNgQRdQJlNdpqRCrRbxluvdFXWasXWmmkqIDmv1utrFQYMMlxHvBWsNpSSsaNdVEJpEWrV4CjFnbo0QxHBo2gAsRnCopTrnRUMORed1iI4E6prl1FCkXm1FF2/OkFaNdiUSJKwv6WrDpa3vCuuBLrjgscHQJNNaRZraYEFjvy/U+4tu/BK1xLodF6exK6Hz+NjBGoG9lMw8TRyOe6Yl0XVRdaH+hj5GXWQ1Ufs4C7kWjVBqhaUkUp24xNOUYhDf0Q+9xvcUHdk5K3QegtNmqBijS6aVYUtzkA0la96dd5oFt1SDmA3OFTqv808fHaHr1HUlWsBYNenGCg2r45ymATnOwGANnSu82BmebwvBFHID24RWdSQXY6SUtsKwM0YKeRaeHrXxWhbtUJ2z2Islv1XFpl2sxvq3pVlcNeODqhpqzVinPIScjKadmB/Zuf7lX/8jf/Offsnrt29Ui1qVBdrMR8G/fvvDn3UpsPpQqa71YxHW72uqY60K3JX1k9Wm9zIe+OHX/TiDrSucdrfbsbu6xjpH48ySMtNi8GGgsz0+WKxpuJYJJqmPenV+pZQpLVOL4fHhwN/+/a/55z+84+GxsguB7pMNVy5znEYSM3PLLEvGN+hdxDTLIAYXIq33BBexttEPwtW1x9oETh9adZtVXE0472jWgreIdYQhMGfh6VCovqeahfuHJ46HifFc8X7RQ8VUDnNmnBs2J17cLNxtDKG3WBvILXOi8rAkOJ/pRbDLiet65FoWnFSSAWSD0NH3fs2Pb2wErr3h23wmt8YgEVd0adGHptrZqIGN1jl86HT4P87k0dL5DTYatXqaJ1yNbO9u2N7eooYgA9Zzmt6zv584vTtxOi/kmhHg+eDIc+E0FaXS5wZFs+7fPD4yLoZ/9/KOwQWEhdhbOmd13IMmFKS0UFvTF8ZajBemgoLACZCh5BlTC4MoUV/05NeUAtRAUJZlfX71WZXcaMaueumkeubLctYqo9T6DqQwl0aeFqyzqiVddd21rNf7pjHxIQaFsawqmwtis8maEdf0naFpgaBelq7grGEYOg0MLJmn/YlSz1yYyKVo113uDxj5hiUvyBeOT591dH1cnd/atdWSWdLEktPaWYPYip50hm7Y0A/rQpiK9ZYuGDZBuzbWUZ/1DnFGAxFZr/NNbyTFwlItS2lYsxCcBjdWKbjg1uLv1nHFRQ6vY8S2LrhqVdG+F0uw8GwjfLIz9B5oVSWH0mh2Xfg1tIt2QpWCzJ7TY+HN28RpMgTjVALX0BFQ0+NQ0MPWrTNvawQvOpLRLK3Vni8Nt/IkpmnSYv1jiutf/Ie/5atvvmVeRi2KIutm/6P86nJdv3wwOn/VIqkjgI+FES7QlI9zWZq+f9A+RNzWVXVQa9F2HKNXevS6W0qh7wKboVtdIY15ySxpJnaCD5EYPdEJUSrBFnIr5BlOx4Xf/PYPPO2PWON5enjkj79/RT4bbtzAjoQ/HJE6Yzws4snDFuqZ+nRgYypdCBQjSKlUW5A8k6fM7BTqPWwiYdshTlC0r6Z6piVRlgSlUVBm5nlqfPnVxNlu6MPC/Z/2tDTQ+UKugncbXaykxP2pcD48YKRyfX1L2A500QMLZWOYSEy1MM4n/HSgTntcZ9ltO4wEWoV5OmMk4CUg8ZpYRp5tHH/+eSAkwR4sOxbyeGJ3HYi9pZGYTguh6zG9ylmCcbw/7qlTJgxgJeG8dribZ1eEm1sKgklNLc0O5mVmOhdk2mNXktRdy3S9MDl9ZAJAsRjpeW0tX70ZkTLTO4fYSJBG8A5TCyWfsbZqFHdRgn5tKFfVoW9QbTTTWGqmisEFj6sVlxZcM0QfdTZaZowL1HW51VZkYKmJS1ulO1C9hWm3BILFRscyLyubuFFFnWMKhF+73mo+XkHX5arqKhUGr2Mc1ndEAc5m/TWc06JqrWFZzozjSM4NsJpLZSzGeEqxtOrJOVHePHA6j1Acfbeh7wLBq3XcIMyzsGR0ptksSKQVaBRidFxf94SIapil4K1l4ypDsLhgwDrloNrVMdaAWii50bLqRCtCLoZcZoyZ8U50Ru4SwyD0UWewRvSzkUtztrJDpGQyOnLsvWEXDS+uhKG7NFw6jtBfyZAoqqRoIFKoGU5PjfevR57eHxAb8DHgReO0q1ZYQgyq+00L3mlx1SXYZXypln5Nos60RWPKa+bDOOr/d3H96utvNeb6UhS5dKwf/58fdpgXXerln8vm7zJDrUVfgovc4lKIzTrD0gZ1HQVctneVVfd6GQ3oUuFqO7DbRIwpmv80LSxpxgVD3HjtAmrF1kYuC7/7w9d899099/eVL7/8iuP+SGeFVhO1QSyewBFfFxKBs288ivAQDcOLnq3tOJ4emecjfQDvIwuKPGtz0Y4+rBnoYnC+0y0umVxnasss5ydKnlQ5UQNF4Olp5tX3M//y9oiXIzfF0LkNEjwtO3WjFDhOZ3ItdGKIztJ1HXE3sDjDKBa7FVI6cZxG9uPCVS7swobN1R13d7f03RbbVNNZXEDsgEggpieey8y2W8gTpG1ke5vZP37H1jt871DOmrCUTD6dsGYmiqGPM9VCw+G7Hf3VDgmOZAppPOvSsmTIjtMZ3n7v+PpPunC6uV7lYefEpvV0RiHHsSWiE5qF7csdXix2HplPSqRvpuiSUKA5xaeJqEa4VFFJj1ey0bI05jwz5koR9ICxDuMarRZKmmlFXVamaqheXhUpSovSHPtWjPrP9Ub5IeDP1AYl413AePsD4tvl+b7IELV41FqZp5kWdGECumNt63ugUdKsIzIHaJd9c7Xh889eYqxwf/8eMY5pzkyzyiEvMCRjhNKK3mZq5f3hzG//9C1X1zu2W0uM8YN+NFXDUgwNh1hPCJExZRDh6mpgM3icNyxZFQbRVrZSCK6BtThndT5aHWluun2fTqSTLmRNVRlaLolqCmHwuKhzW6Jh2ERccBhU8oQ4DJoi4Cyrnj1r12+EXQ8vt5aroYEVarOqJLCrZK3p56dGt0xzluWp8vDqRD4nrn3QEdfl78caykoUe/4i4HrLaYyM58wyLTqPXvdGpejne2n4Sm46C26ClR+Z/nqeTh+SFA0XilVbjQGXzvVjkb0U1suP/ZtCu17311b1Up7Jua5fS7iQsS4sgo8dcF3jISwxBG5uruj7gIhe8+clq/vLBZyLlFKZppljTRwev+OXv/wv/ObX39PyFXmCQKO5TC2LhumlhVOeGJvgzB0B4WArOSxUmSnRMw+Wc5q4chrpXIsOvFuG5ize9bRamcazkrC8X094g2FS5KEtlCYYN7C5/inOTXxx+w3L44GyNDa2YXtwww63WB4fn8hpAqNdw4tnd/z002dc327AN16PTzyUkV3cEKJhSyYtC6E6+nCFcTdMZQOz2vmKCM04xKtAv1RDqxnfJ2SwSInEDP3OY9OI9w1Mpi1N8W2tMKUM0eO2llD1Oi9DR9dvOOeJfN7jp0SaC+c08XTI3D8kvn695w9fv6IuB/6Hn17z6XUg9oGSLfvHmbRkejLbIDS/sNgDpAljvG6uZaGWxDInZUswIC7guwgpkWrRxeKiQOYpZU5FOFcdJjlEOcNAyRVTC5IzzAlj1jTQ2mBVsNR1da8590bpSz8Q/QtGwwJLUmtn02tpLYosbO1jAyHSNPMprwdC0Xfqh91JY20gjHIwXNBU2BfPrvnis89wduD59UvuHx/4/u1b3j8+YqzgXGBZKikVYtT5fzOFivDucOA3f/wtz54Zrq6+ILqeWiCVxpwqaS3Ou90W7x19hGd3HTE6jcqxlt54tl7YxBkXdCZqDNS5cnh34uHNE+fDPfM8kxOY2eOqR5wqGrbXG66e3/CsOzLYmWYD/a4nOEvLjZLU1NAomnTQK/O1OEu1Ce8zV73lul80BtyIgnZUvoEx4JzQmkL4cyuc9wuHtyfyaaIXiNtBi31qmmW2HlzbwXF75wg74aZFDvvC00NjPhnmc14PPzDGfiiuVoyqKtZl+48qrrUV3VJKW7vGj/NU3WR+/MKXUcFlrvpDSZX+d/0wPrg8T/o1L1+j/eD768zrQshqHwu4wRJjx/VuRwxhVSVkZck2g3OdOlScLmR+/8c/8uU//xNffvU9j0+FDQd6PDsr+Cg0DMuSsbVyynrqJxsJpuHnE4Nkomxh8NhPrhnPhSwWWUByAxJZZgbXE+IAoE6XaUFkZBiuic6R0klpXt7hpCNuP+PZ5/8zzh45Pt4zvX/PeBioxnBaMmM9UPDUcsIw433jk83Az3/ygj/7xQuefzYwych+PzOPExISxhnsnPAGbgP0diLNC988PXI8zJRSyPlEFx1ffPEFsvEccuI0F5Y0stlFdlbYNTVBSAyEztBI5HGizhmqI64sVOMLLhgCEesGlmXi+PiI8+DczHSw/PHVka8ej4w1U0xGNoU0Vs77wtFbbm5u6FzkfnzH0/68gjQMznjmogenNRaxGbEre7QavWIWQxVLEWGxsDRYjhNmKswJzsZxsJUxruCVc8ZTdKtdVEsbpUGwzCKU1shzJhc1tOjivOqssupNCNAkUyvaXRblGPjotSlAF16X98WYtmYwtXXkAOplR3WvXJxm8mFTbkzDW6MkpmC5ubpi0+/o/Y5dtyNYR1kWdkPH5vqK87Tw/fevEFPY7Aa6PnCczqTsyOOJ/ekdh/MLqvkCYy1UWJbKvOQVOQix6+iGyPVguOpV3YIIFsUbBu/xnSXGjHeOusDj6yPf/vaB+XDGtEwzosVndXg1dD9jraWPjl50zi3e0UVHWyrjceR0mJlnTYUeth3Xn9wwXPeIt1g5c91nNv2G4Oyqcza0ZU0raSsLdt3jYBvnpfDu+0eO7054wHaeanusdczTzDxlWm04Z7i56djsAhIFY/sVAmR5vD9xMAsGi5eg48hFdcql6jPxYT7+Y4qrMQaxhtrUNHBhraoldZVGycftvz4w7d98nX9deC+F8uN89uPPNf/mILi4vNY5FIau67i6vsZ5q4rDkpRoVAwiAe8DcQgs5zO//d2f+M1v7nm3V6tfiJXOGmq1GK/ur1qUVE7rVgveTFsMcQY8sGgcjQ0dVaJ6jTs1NZZiqMbhgmPYdvRbh7H6Z33/cGBJjefPepxvtOKYSsb5Dvprau85VXif4Gk0lNFhvGHKZ8bxCWOFLupMK1fDze2On3x+y89+8YIXn28ozAwPjvt5yz6dOE5nxrkwnhbEF7xTqtZxjjydZ87v3lCOb3j54hn/YuG9Mzy0eZX7FML7wl0RPvcd2+stQ9dRjUdsh+t7JpNoMwSrm/3mFlpniX6LE895uielI60Zcpo5TYH7+wO//eZA9UJ0C640vvjklrtdTxg65qpSmt2mR2rC14opBiMeAW6vtgzicS2RSwOJOIl0ndLXpvm0ZltVnsaZ8+OJTROK9+w7x2NsjDkjWYE5fRM6Z7He4XKgKxVyJVFpeb2er+aQiwXUXHYArHd2LmqZRqbSSoGMLisxSLuoC5Q/7JzTZWazlAK16WLEsD7XhlVDadf3rhK8arNjsPR9j3dBZ83BYeSWfvBUKvd7DRm0Fu6e7XBBWNJISjOWwGYTeHEn3Oyu8DZyiacfp5mcyyp7UtHLED27wWJF87qEBSMZaw3BZTaxEKwyBQ6HxMO3R/Ix0ZlAq45sVtts0yKXWyLnmf0+UdORn7+0mN5hbSOdFx5O75n2M4enE9OUECpHZ5gej3z2i0/p77TJiZ0mVDQsxloFslj9nLSZKeSs9Iy8VO6/P3N+d4JUKOI4N+UjbK3FwMoMMFxdB+4+GegGt17BLTYC0uk8tTR1coqn5YRxFpuElistr7B2/m29+/9UXGv7WEQvKay09nFj+q8q4UfGgPk3P/Z/90/7cO3nIyD74tBQT52e5kZWHZ0OvYKPDH2PFaBVSk5MY6YUAau2OecF6QJd8BhmnJlxTogYfKl4b3FWsKaQ0eF2aBZbMzmNLGajS4JxpL17grRgzoW+NKQuBF/YDkITFbTbYDC2EftI7D3naaLNGeMziRNtOVLzhGuOEAaa9yzzmTlP7Cd485gZasUbQbxHU5jArlEcN7sNP//iGV/87JaXn225uemos8Cy47bvWCgcpolf7b/m+3d/4FASX9d7mqjrayORu1C5++Il4dNP+WaqfLs/cSozlkw3GDor5KeFuT7wybLj2e4Zu6trJHjePp35/ukAp8xPd7c823b4ZsBUPBk/GO6eDXQ93L99y7g/0+YeIwtzHinFU+qCLZXbncXsLC469o/v6CQSvWe4CrjayGNmXGZOS9I4j1BpJqvDyHY4B42FeTkxpUrzPcelcFgaPva0WjgHuO8qY4BiRfWbtbGcE2POmupgPbUWljpzLom0xgxdCqeRdY6HJq9+WLk0paFV0Zw1g6MVs0aK6w5AITCAUYG9weJ8IES/rrBUl9raRT2jCyLQ1Zm+BhqkV1KDClbUGeW7DdvWcRxPfPfmDVe7Lbe3V9y/f8vT45laC8F13O22vLy95mefDby8eY4X+6E5mpekcS5cbo+qX+2DQ2oFVgelEVywbEJmG7JGaafG4d2R0/szAWW7VqNju1KsMnGDw5mMtxapUOaksjsxlLRwuj8RrRoOfLNqIKlQxsbp/ZHT7Z5ud0O0Dqol7xcNoQwO3DqyMAXxSrqzDdKSef924unViCue1nc8LI0/PU1sJSFDh5SEMxB64eaTjs2VzpYxjtZ0DtuJQ657ooOUoOKZF6FOhjoXbK7kqbCs/NsfVVwbl25S54ZrEAQXPRyrlfXSwf6wo70U2R/yXNu/Kso/HB98BLfodao18yEaZtUgIOJw1rMZNuw23cpnLaQMS4La1I4momCJ/5O0N2myK7uy9L592tu8xhu00TCCzCSzymqsgczq/08kk0llplSlKVPJJIkAAnB485rbnLYG53kAZOakQpgB8AiHu7937j5rr/Wtzabnv/6vf8++/8g//uMH7u8EpoyqQkKYUkGVQk2NeO5E8Bi8alpTCgmTM5s5QWnsgHFwDFZjMBgjoCPGFIqsFFZct6MbXWPGKhjGrrEP9IhgkaqaLICmzDMl3HM7LlzfGOIxMefUttSk5rWrlaG3/O53b/j9f/6Wt99fsd85jBLEOV5e3/zy5pyWwHKXuTMfWWPiOK2sJrKsn/Fi2N5u8buBh1z46fHI/fkO8QlnLbFYkhHOeiEtj9SPJ8opUTHMSvjf//Rn/vJ0ZF81xvT0Y8emWoiRSZ0IKbamUzEYq8k5cpwq56wo1rGeSpt4VWYKidPTSjeOGDVyns70145x6MnrTEkZXSo6wTRHJipWVbS4y6M4EcPUigOrQRWNzoLOhcE1q01RCSUZL47qLVUnckyEKRNjQWeF0ab1jBWYciU+TyLSFlAtKdQ8r/K8NKqt60krIbUXMaYKfYWdNigMc2qligmDwlFya6ctKmEuk2Kt6Xmb1X5fKlXVSwOEIsVmmnfGY3TTy1tMvGKNaa4Hq/n93/2WJQb+33/+Z2puG+7BW759c8Pvvv2Ob19f8WK/4Wp7jfUtrJByIKRyeU/pxq+tuem8qnk8tVFE0aAK3mo2NjD69j1Lq7CcltaoIc0KiCSMND9olmb/7KzBammVLiridEXVQg6N5+CH9rCRTdP+4pyahl2b5VMEaoTDpycePh5Z14j2hv7qipvXL3GDBnXBkKI43p+4e/cEQej6kacE/3ae+LwWrncDlsZLkE6zu3Vcv2y9X6KaTzZflElVBeUqVllyMUxJcThGjnPbA6laSBWCGHL6lbLAM5FKifpqKlXtsLg4CJ6nzF96v79yDDwfnl8qXL6WBep/+DHPCzNoNgl1efoL6qKDVTbbge2m47kbLITSMsNLxEjgCqGT5kX9zQ+3/Pjyf+HN9Qf+t//jZz5++EwNEEQaHSsnakp41WhHzggOhS4BakYXTb8URico1x4w1rQkizaCso5u3GI6Tb/psE7T9ZZ+4zgezuRYwAzM4vjp/MBhOjFwYudP3Ly4xekTPqx8u1V8Kon5fCYmi1PtyW+M8PrtNX/4z9/x9vsrxq1pTGXVPrerlhozFEWnCzup3FbD3WPg2gy4ocPZyrXVWFv56fNnHupji/DWqQGlq8GYLRQoZWENiSUUzvnEqj/yp+OJf3maCDXR9Z7JJA410tuezmuUnIlpRkuliEfsFt1rpoc7Pj08Ec8gi+CsRnWavzw9clw9s2iiSqzTI5nIOA5tOVQKIRVOKzzGSkfiyju0cShjiHGm5gRoatGQE1vtGLYdKZxYc0TVyqgVS4RsK6IdE6V5QasilAYCKqJYtSaXVkMiSjDKXKKoLYd/aVhB1UvEuVZUafbA69Fx4wyvesfOClpZlqR5PEWmqJhzZUoT5zqTayDWfGGWPi/LgNpSTi3z36xMToMzlqEfW6tpbctEc5HJtAibwTNuXvHw8Mj9bstuMwCFzdjx7etbXl9fcbvf03c9xiiUatv3GAtraAc+WIwFqxKboQFlahasdU0qI9BrYecMzjT/asyFFNJF68yUGi6QF35Zm2hdcNZhTUHSeyUWAAAgAElEQVQbYfSGjQGLpqRCrpnsM+JaG0KdhFRaTxhWECukkjjcnfj4x3um+wWhyQGnp4USCy/eXqH7NtRNp4m7d5+Jp0A3Gh5L4Z8/V96dC9fDyHXfYdNEUZlu57l9OzJeWZRti0hUa5/OpfFdlSooq8hJ8bREPq2RpxBJa0QVjUJhrXCxCP/PH66XU7BNqxeriABGGzKZnPNXssFf81ufD87nw/Rvp9qvP+7r3zcnAr8YrJVSlySPUFioSjFsRvqhB6nkCsuaeTwceffuiWGrefvqFY4eUyq9JF7efEf4L6+Y5SOf7o+cT5H7T3ek4z0+aupUqHFBVEREiGnFWMfYW0Y3YlTC6IBSheVcOZZA5xV27zBOWm+9Ns3Mfblp9YNvpXD3B86nE++mwP/580c+nc+MKfKNf+C305mt1ZzvEtf92PiQVlgX00zy3YTtPN//5jUvX3V0Xat6VrW2mGZeybWQa6VkUCrxzesrzj/+AE/vOBwm4vqAvXIMr64pWpienog5oE0LBlRlCWtlJtJvHFqPVNX0zDUWjocn7h7P+GLZecXWFj7+/CfqdMVV/wPXu7FJKyogNRNDJMSMNpVxU/m7N57bbktYHLFmDpfE01lp7k+Z/aD4/tUrYlr4b/9yhxHD77+9xXhhPi48pcKSM2Vd2HZCX+VirG+m+EZJi5SLpy9SmGJmoRJqorgW+wmpdSktISEJYqmcUyRLW1AVmv6o0HCJrcpz/XKt6FzwCsa+Y+M61hQQV/iHH1/xw5VnZyISJmKAJSim/ci8Wk5r5vNUuZsjE61aOl0O6VJb4gsBJYZUm36rq+A6y3bcsdtc45xvXtvL1/gsUGiaVvvmxUuutru2+VfgnWI3eHrfCGJatZZlqa1Y8rwspAtfQWkQ1ehVnVVoYygISntUzoxe2LjCpq9YkzBiEJoE4J3DSFNnRQRjL2eBBmU1ph9wncLoM72xaDbUlJFmEIZOQQdEmjxSNaiMsk1mOz6duXv/xPkhIlFhnKLUQjwHTnf3jINh57fElPj8/oH1sND3HZOCf/p54V+PA53d8ONY2NJuVqa33L7dsX/ZY/sGjCEbFKoR1UpLKGYRUlU8Lon3TxOfp8Q5QqoGJZ7eVpRtkeVffbiWWlqbhLTWxl8Avhfb1H9wFl900i8H5l9PpV/+268P3C8T7ZegwfNjsJR86RRP5DzQ+w7nNVllcgws84nT4cQ0RfpBQ774HXVrBtAiXN32vPk+k7xllz37q4H7P55Rpwp2Q1ohrwulJmptojt1wTlN5zQxF2oQOm2bZ7IIVnqcVoDFmI7tbs8w9FxwGyhT0cwQVkyArigkekJyRFU5HWd+eqx8erfyzV5zezvQGcPplNGDwexHxs0t3377irHPGCktWVUiKkRO05HzfCaWCz5P2vZ02HUtvXUqUCI1VhSa7faK7X5LKisfp8pwOvExTDzGhbOO6GrpxTDaDRul6I3g68rvd9fgO65eDmw2DeVoxbAde7zvW4yQiOS1bbrDmTgH+lp52zs2i+JTEn6OwpRAux1LjDwezrwQQ7cZEbNHq8JyPLBME5tuwKBZ1oLpfWOupgxzwIhgbU+skdNyIOtKKJlVrWStiMNIMEKAlhITeDpO3N0/UVfVgN4ps6ZMrJmqGmPCGI1R5aLnNUCKEnBa05G57j1vb2/ZbwaKTrx5u+E3rztcfCRNK2GBoCL7oQUQzlPgeIZONL10nFJlTsI5F+YcWS/NyGJaDUrOBSOGrvPsdz031y/YbPYY61DaXPTb+gtXVtVWqmiMYfAdyuiGstTyi2T23BQrTeMgrIXDcflC4ZdMKitjb+gHj9LNt1koWCtsx45dHxm7hgoUrRFdm1tEt+FIi8IYdTlcm5ujcHEJDD2jnhlQ5CWzlKVFWseOo7aA4JoijVGFogveK0pMHD6fOX1eIenGk7CCNY5OKaxqpZhx6Xh6PHL6NLHpBx6K4v/+eOKfHxr/4Iet4ruusE0QlWf3csvrb66xYyu3pLRG6VIKUiJGMlllQhVOK3w4Ru7nyjkKAUu+NDJYXZhz+/f+qsP1r5dW7VBM6RJbvfzx1/qpukw8z1LB80H5ZUEFX+uuXzsO/lqH/SIrND+hokhClKUzPdveY3WTDCoQlomwntFWN2OyUWDPGLu2GgYN+63nt9/tOMXM01Ph7csN+tHzcJ5QTqGVQ4wmr4UQAyFUOp2bx9J0UG3jKlRFiAVnFDUeiFPE6muMbLC2R8RRUmaeucgnEVnPbGfDH4znxc1IsR0bC52Gdw+PvJ8C0zyxrI7rzrMdB+ztBr3Z8uL6Nbu9IacWzaxlouRISguPj4+cz0tblHQjkcw0zxxPJ7RLbPYK77ft+4HQIjknBm/53fULhn7L9OEd97JQXJsCbar4atmOW3SJuGrYjh2+0/QK+lzpBkc3btiP2wYAp/lGS1zwyrLxjrQ8supIzR0xJJ6OM/92PvExZIzu6LVBlxWVICxQBsc3NzekLkM9USr0g8OdNWa4weuClRmLJS0ZRHBdh86J43LmWCPzaEmdA+NRVjU4dUks55XzeSVn1erZTZNIdKXBoS/waQqIloYVoGBF6KzhaugZEHZG89Iqtr7y+s01NzeOut6TliOaBudOMWBNhLyw7To643DGMjrNEjVTgPsQOJfKMWSOYWUNkUwi5kw1gtk69te3bPdXWOeo0hgHuYC51M03z7n84rU0WmO1aUWdujQw0QWOrbSgueiKORFTmzaLUkRpvAJrDCFUDvOEM5qrbYvpepPpXWFwFW3NpRFAYay+TPrtqua9RRuex4pWM68jnVF4EUwRcprAQFWG99PKu88Hvn+54bfOksLlZ+qEsXcQCsvnFVlbWAGX6XYdfdc3kA2ZsAbuPz5w/+mMKZXSd/zpUPnXkyJk4Yet8JtBsUGoAtt9x+vvbxj3FsxKKQ1T2SLKEUWkqkiphTloPp4zj9GSlAbVUpIPhxltNGbTE6tqBY2/5nB9Bl4Dl3qSxNdM1lLKBc31BbJizPNGteEGlfpit6r167jsl0P1bzGFXybZi0wgF/mhGJzx7HYjg/fY2kb5tQhuc82majb7gY13bKl4k8CCUoUuRzoyNWVULQy2YJWQaybljNcOoyo1rQ26nFr8VZuCSKFzFkicTxO1avzQ0fWaUAsxVuJayLFN49MyIbPF+sJxKnz6dGL9XNHeMQ6Ku9Mj3f6azfXAD2967u4G7t79TMfC9vU1V9cb/ItblBvYbBxWZ8oCRS2sZFIKKMlUNJ3boXUHqXI+fmaezwzW8ubVwLoVUo0cjjNxPrdEUw1EVTid3/OXhyNLWOlsYdSamyy88Rtu+y2+COeHmSqZ3QjOZmoOzE8R5gmfI9lCtlusbnzdpLeI3WIl0Sfhab7nw1Pg/hxJHkwpbDvNtbb0uSEt1TC269bDgc5mrIBTA952eKnsRliOn1i9o9pI5IygUclBdKjkcLXSdZXJFSZdMbZNb1UpSoR1jsSlghi0d9RSsMYioikVLK3AsZRM5xoAW+VyIWgVbFrYOce1NYyS6EqlHBNT0ThbUMq3SLE1bEaPEih5pZSC922yNLIyTYXRGDpreZgLrgg6Zc6lMK3tzYuFOC8cn07UFCh55fXrV0Rp1SRZ68aXvZTqPUtoUpsNn8uNT5Q0ctPlQSEiFIR5zsxrbpjBWAlJ6L3HSOUvf37PT+8f+ebVDVe//4axM2hJbH2m79pVnyo477HOXUAw+pe8vTblsvVP+Nosh97aSztybCxZbZkK/OvdwofziTcb1zrPcgIl9B04L6zHCJPCYcGDv3aM1z0qa9KUWOdAkEg8TNRkqM7zU3as+5cMu88M60f+bqd5YQWbNXZrePmbK7YvPeJbKlNXjdKFmOc25UsAqUQRPp9XPk6aQ7JMKbbQxZJ4fDyijWCUpXf+cnP9FYdri9R9Aal83Q7wZbt/IZBf5NOvr/jPBzM8p7W+JLj+Vo+ttbQ0GM+NsM//v0ueu2q0snhn2Y4d7nLdi6WwpIJxPdutou8yqITWPe6ySSx1oZaZskYkVEzKPHx+z7t3PxECOO3R1tAbkFLw1rEZ9xiZyTVSSoCL2bvve3KGXFfmmDivCVsKuMx5WXDbHmOFHCdqKMBANi/4PJ/QwTL2jk6t+FzZhIDrPD9++wqZJ3od6LYD3W6DGzp67/A2k9cFrUvLyMMF5WhBGaqCJStyjjwdzqynR5x4dsNA6h3ntaBVD1FTwkRWC7UYDqHy6XBAKc23RnOVNFfaYaYAdmWOlXle6TYKJRGroDPNtG+NQTsPxlA1VK1QekPNMIcWY7XeMQwj+1uF6RMxFIaT5eMh0JlCHQeWpHmYV3ScGF2DxQz9DtGQQmtdGIvHKSA19qZ1mpQLORXWupJyoZp2VdVSmk82tXQS0tI/y6m1uhrTQCHLvBDW9hCNMV4msGdHi0AWvOnoncXkiNOKsR/YOMW+s9QysZxO1OzJg2XctmXRZYzEOU+pmhBXalU4r9huLd5Ecsp0a8HUTKcqvdYE2XBeLIdpIZRCPZ25m888WDA1sRtG6DNCRRnBiiNrSKY1n9pnYJxSTQLQGaVa7UmDvig0QixwPK+EWIliOceVVCqdgfl85u7TPU5pXt1s2I9NDNVAbwXnGkeg5oZq7GyPVTMi5VLl8oVPa1TG1EiVFa0MTgudbo2pRWs+nOEvZ0M3bNg4S8ltK6RVxo2KQmI6BkLWiCr0o2Nz06OcEA+R02EmrImsK2KE6iz/8jDxT+dCdE/065EfbhxvvcGXjBkst9/vuP1ug4xQqNSsEdoyVCnBSpMY56y5O8CHU+EpNyknXpJ2a2xLbsESUsHY5hr5VYdr+Qpi/fV0+eUKry8f9+8XWc+/vp5O22H7HJv9Mv1+aYwtFwtMbeR1nmHCrV7YaUvXWYbRg65kiaQUyNOCixEtmjGcUXefydGReCCxEkriHFfm48Lrh8SSNU9hRWE4nid6p+isYtj09C6TnxmSxrWFhm5Cd+sFal+zkkzvLVvfU8Vf+uVhWhZSPCO5EYfOj4HzU2CKQBDUsbLdOXYWvCp0xvCHt1u+3/0DdTlincH1XaP+5BMff3pEpSPbQeE3I2JHRHmWHCg1oo2jGk8WixmvqVhUhlQW5vBEjoIUTa0R6y2dKBaEx5j4HAs33vONWMas8GslpJUlCY+PJ9Zp5tvtDU5LawZA0Q89XX/D5volZvA463FGoYqlhAMs99R4JOWFoRv43Zue8+GJdQm87EcG4/jzU+CYVnRR2BhxFLxRF4V9pZQE2mK1xZQGZE95AddeR8ZALJlQYBXhWApTKKy2oDuaZSm1h29aMutSyEnIJTU/dCoXna1BuNVXlkEuDOKWvqmUUHhaAi5Vdrdbqmh6PyA6YLwB23rVnK74rtUNrTGgpAP8L4EB79vDIy8RVTJ6VIydcF0dUQxTtBxmz7xGYkyEHCkKzvcH/jX+kd31NTcvb7kugumb5U2LIWtFdS2YUJ+dDVKBRK0JY+wlqy/EmDlNC1OIPJwXTlPEKdAX7bd3juvdhm9f3+K9ZVkzg6/s+xYe0CiysogVum7AuQlqQEyTAo1pqMDWWJPJtEPeqcLGGayGT0vhj4fMUYTXG0tHgZyxSuhHRb/3xCWxngupKpytJGv44ylhlbBZC2HNUFpHGF74p8eF//Zz4P68sNEHfr/3/LAd8akVO/a7DdcvB9ygyLrdYEQu8BZaJRVFiNVxf8q8e0rcB8cUoKSCqpBRTUPuPcZ6nG+w9WdK3//04fo8sf4ywdIwa//uYPwPvKzPv75eYH193f/a4/qMHITn+B8X4/Xlz+rFnK0ioipdb1FWQV2RZcJPZ27mCRdhOCz4T4+c9UrigUNJKPFk0WQRdlWxLSBzoEvtG6pSZV3P5BzZbi3TWXM6BmKqbHvdssi1Ms3NWG1cxXtF1xv6fkeVjqQUSkXiGhEWvGtdR5IDUgKUBZUDXRq46jr6sVAkIucndOgYxWN2nigODXgVeToc+fzzz0i+J+8tQ7pF2QjiWMIMEhmGLa5v7NN+2DD0e0qsrMe/UM8Q5tSo+w760WCL8HiK/HmKPCjNkAs5tQmglMK8rlSrOM8z3hqMbiVtCkAMxm8ww4jte1JtcBSjLKbT2LySiyIVTaYj5YpQcL5dSXVWeNcRSuHDz3e86jpejI7t2OO8IcyReT5ju0jfbZhz4LQ2r6P1lpQzT4el2d6VJYsmasNKYpFMFtCqkspKSq2uZV1aL1QMilwia80Y7VqRYYmtUgWIIVyWPoUYAmjdDsjcsIFzKpyWjJOAvXKMrqPEhUJmzRrVtW4tiqPmzClkcm6AanfB9CkjKG8oSlBF6JQiI6xFscmaTSesQVNKZc2RUCvTmrh/f8enj0+8//meV29u+PHHW271FaI6bKhUXxED8ksBaIu9iWoRT6uFnBUhReZl5f7wxP3DRMHivMEKmL6nf/uK2/3I2HtEDNZ69n5i9M/vUYtoi/LgNh7dVWpsEkQpGSUOMb55g5G2C5hgYzV7rwg186dT4mNoS6vXgzBQMNIcN8O1RXvD/JCgGowG3TneL4H/618/82az5XfbDpstnWmEs3fzmf/nU+BhNlybzI8b+K7X2BCouZBU5HAKjA/wYvMCYxxKmWZJI6Au51EsiuPquFsSh6JZiyLGSM2N17vGjLEdw9A4v9oKrck3/f85XC/tiMhX0T/5dwfp12GCfCkYfD5w/9YV8LW3VWt9WYRdeoqeh15puWzhmVJeUAq63tMPwy+1MWZd8cvM7fnAsDY0mlLSvjJTSEp+saxY06jnmYiTBdNB0ZWoNDFmHh9OpOCodcfhFDFSsMqha27ZctUjxmE3IG6h6ubnExXpO4NSK7mslLKQqM1Os99wOsDRJYzAZgvbviI1EaPF6T1GLSzrgVJ7npaFkgOCIq2WzTBAPnA+TyxzxfcB2w1oCzFNzMeK5EpVwpoKsRjiGrGsONtBXzDGgm4ptOQFKe22UcQwUTilRJdMa7bttyCWuH5m03UtIVQEY0cymlAUklbWsFBFY/xwmc6kpWd8B1UgF2o4E+tMKBm0ox8M11kYnxJ7o3jZWzadJlv4lApTEEqEvXiq9ZzCQmDF6MYELTEjtRndtYWEsOTEVBcWlVHVkLOmVAEKNSdSWNvrjeYIUJc6ohAiSMaYlhsvz6/BHDEKOmfovQVnKSGyCpyLZsiG+8dzy6bLgpaMHrZMUyLXFd85QoF//OmBp3Pl+73lzZVmHNrBgxKMA6OkkZlywpXKWA29F9bl2Q5kiEVxmhUHJ3w6rHz48898vPvIFN7yD3/4HTfXFXETNm1wxbWamqLQOvELF7XmFrstcJ5mDqfmMFECXoOmpa6MCNobhsE3KMkFKr7rYPCmTaXi0Eq3n/vgUV0rw9RaEfNCqQ4jlnx5v6mqMaLYeWHoCp8DvJsKc058v7W8sQZ7qX7yg2K8HZpPfr3sZrTiIPDfP098fIDXrkJoIaaE5X41/OO94n41XFvhH/aWb8eKrxqWRKwXDXXOfP7pM64fuHk7oiiXBRbNMVGF82r4+VT4OGuOQVhjG6RKSUzLSkgFsCAWZ8BK28VY8ytlgecK2Rgjz9Pm8zT7H3lanyOs8EUO+JqM1Q7aL2mshi2TX2pioP29VuZyyDZNt9TcbH5a8K7D2wGFapi4FAnhSFanpgsng3KuCeGlUiSRSqBW8Nq2H3pR7Lsr3t4W3j1+aJvPXJiDoWLIeWHJrT57nAOqFEpSpCQo32AORhuK9s1nup4wHeyGPYhnCoUlRWY0qu8YR8XttmD7Sr8BFQNqSYQaWIxFnEYyrPPEeixMOfD0EOm6F+z3mpIt6xyoteJoPISuc3RmpNIOwBhmagzkMLcbhaloY9hsLbUKIUZKyNQiVDqkRoxWLCrxITSWpauJvh/YbwZevnzBdmNxrtUPYyybfkDUTC2BdT3TDxuMhlIjNbV66piFQoc40G6iTpE1BAod2lk2N5YfY2GjHGOviMBTqZyN5hwy6zEiaaAUz7xUKpGs9aXGo2JQ5NJ+pnNZCc5SbMvGa2MptT1ctS7EJRBjswaWGi5wdtWo/FwaVeulIrkWasmIgNWueWhjAKWItblGntaMTjNqU/huv2OjPE8Pd0znIxFLJnFaZx6j591TZZ5nXvSJklvSx3hHq9VWFGnFgRaFLpVcBWMqVkdKjCjdorWmVhSKnC1LKpyWxLt/e0Crd7x+eyR/d4VojfUrBttM+CKIasQ4VdvbKMbC8TwxrROlFnrvIEVqqlRryKX1WqnnfUcBK5HRN0iO0paCAU2rC9o6hq1nOp/RNCwgtcllTrWhKOVKToneBIrR/OXR8LQWdsbw28Hx0mosBS0wvujpr3oOPx3JoWCMYtGKP8+Rv5w01m7YeYuTStWJx2j449PMp2Pi2in+087wwwZMbm22iYKyIGLQxbAeIg/vT+z2N/j9pflAtWt/SMLdJHyYhacoLCFSawstrSEQU4spp5xwVjMMHnvhhwze/brDtf16Dp9eIBZAvVQLl4vQ+3UMth3K+q8O3r+VCr6gCfmrCbbJDO2T/HUXV8FIK07zpsc7j4iQBI4lcU9Fdw6XCiq2WmRSbnYtdbGrpIQuM7JWpPT04nmje74ZDauplKhRSajFEHMhW8NUKsdoGLwllsAcAr6zBNPjjGepgkkFJc+VHKXpTK4j6477U2D0Pf2+YzmemfIDMVaQDR2aVM+k6YzSIyV3rHPCSgfFscyf0Po9ovrWW6Y8/WbEb7fobsQMG6TfY/yWEldcjXRhZp5O7VpWAzWuaCwxwJouOfUECUs1ClUhVc1JhHSc2RIYhg3OKXZXNzhTqGkFbfHDNV0HUtdWDEiippnj09SAzs62wyppSoKcJnKZiGkmlspCJYaVwTm+f3XNtS0c45mHCtZ5LIrBGHzsccFTsoZqyNWSqqLWVpCnFFBaa6e2GrlUvtjdBr0dWWNpD9MSCGtph6tSaPtMZms6q7eOlBI5rpd+KnDG4UTjdcvXxxQpVFJpdqWJQK81MvbY7UApkTj3PJ4TIRti8jxNJz7MZx6jcG01vVaUBOtayDajnEFLg0un0v49yujLKkJhkxBrpfG1As4I214Qa5DOc5jh6XTm//vvf+H+rkPVH9G1p+sHuo3HXSY+pVq1S3PhVeYl8HRamdbUWl/zQgkRMY5lDRipeN+1tBStHLCzmaFTLbJbhSKN91pF0Y2Gces5vw9YMVjdkUJqyENjKBVirdiyMtrMQ3S8PxWomt8MAz8Oho3PIIZ+MFy9uWpsgrWpmFkqh9ISVscg/KZT7JxGSeIxwZ+eEp/Wwu3g+bsXW34YCiZMLCEitbZ2YbFtR1Ibu3Y6BKbzGb/rWspRWqvwwwLvJ7ibK+e1tl67Kqzr0vRd1GUozFhn8V7hDHirebHd/rrDtdmvnuuuC0ZdNoaXK7x6tko9R/ku8cG/jbn+bXhAa/3LFPv1hPslmfXMLvjiUCil0WmGsVFslE7kmpmApK/o9w4/njEhNg9cFUoRrOg2/sdCSc/LJgelY1Ph1TByT2QpBS1du47HlZjblJMCTLG27iMjHE0lVgVZU0+NAXtzNbAzI+gOrStTybx/eORP90durh23GKIvPJ0XjqHyZLfsq2ZTPKwLa05k0UxLK41TZublC8vtK4W3mvO6obqRbugx3QYxG9RwhdteUWo7eG9ur5AaeLh7RwxHNBFyomZNCFCMIEsz4pfLlViJIYtBvEMXoVeWq+s9flRUvTDXzBoCTixDKSynIyWdGUaPlEJaZ0puFH3jPM51ECHOK6f5Azk8tjeZWD6cJp7Cyssx8speM+iBHCNRrwRmwlpxSVEloXWr19ZGkaJhmheMQCcVVIshFyWI0RQq3TigdxvSpdE1RljmwDpnUmrIzGG0lO5S0V7aYW3EUFWDImvRGOMxyuCMbuQk3ZKRJRdyiFivMWPH3bLwj3/+mZoWjmfFOVhKdaQkHIPl0xRIaeZqK5RkCdGSpKJ1oKuC1mDEtEVJDe1BpzxFLg8poyFlcg1oU+jdxWrloHMaK5b7w8rx05Gf/vgzVhm6oWMcewbrqLXhD4VKkUxGOM4zpzlScejq2z5AFChDiIVYI/tdj9bS9FoqYydsustGXFSrAZeCqgljFOO2w3YaqYJ1vr0e6jOwurUoeFVZjeXnQ2VJiZfe8ONGeDEolIkYb7l6tWNzPTI9zuQEymkkKx6myqdzw/vtndCpwpKEn8+Ku7lwtbX84Zsdb7xQ5omcEkqBta0ll1yoZLTxradMnnXSQiaRS+MFvD8KHxfN6RIJNtqxxkBYSmtSKBBig/V4a7Fa4Z1wven5/purX3e4Upt/1WhzOWifF1DPB+JzjYtcsv+VkstFnv3rSOxz4kp+6cYpf+UyeE5l/UfMgS9SQ9u6eqcwCnIo9AK/+faGbXeN5oE0T6xTIWdHjULImZwi5IxOgZqP6BCpU+H+AQ5TIbvWFZRyW6IJbRGx9R29assCZdoENA0KRkuuK9PpxHKcyFXhxy2bveVqf5kkDwvoiU/TA2DZeGGJis9VeFoL06r4Rlt2IlQND6cjnx8q1swMfVsk1doRs6Eohx0vPfbJMGxesbm6YRgcy7yQtcU7zTxFYliI80KR1gdUxSK+4OioNRLKhNaVvfecqmJVjgYOts2E3vWsOvJ5PbHkyHRa6dczVc1cCzil0GQUBW0UzyzNMEfC+UxcE8s6s6R7So6U0rGg+LwufJ4msmi0M7ywHdZVfJgYQiFMjho6qhiKVEJYiXGlhEjNobWm6tKsX94SBY55ZQJ63ZOWibxmlFakHJjPKzHkFhd2FWcbvSrFwnIWUqh47S4Hebh8vytVFbJut6/OOdIaSDm37qeaeFpmzucDf/rQwBdrFmLRKB2wxjKnwkzzx6YI57litULX5g4xJaJ1xTpa4rAuSG3ELKPLJXniEUuLe5vUUlHlwvPIEdKEVc0AACAASURBVL2vWG15Oi4cPn3mg7eM45Z9f8XO7MEqRBtEMpnKGuHxtDAta3u4VIHa4tprBErCuxbZdk6hpeJMZT+o1q6smwlfqwtOkNYUMex7uuuRcD/TafNl6lXqUo2dcc4wL5aHeWHfa/5+r/jNTcF3gWoK+9srrl/t0VZTwkyRtqhaZs3jHJjmTC/CjddYhI+z8DAnXtx4/tM3PW+7APPCsaSW4pKW9w+hoUjBtIcChb739J3HmDYIhlD5dEj8NGnu54YtNMailbDMC/MS2sGaIKZKb9qtw4iw7TxvbrfcXvlfebiKagRV3Tp66nPsznzFCKitI6kZ7xP5UnXREiTPeuuXKOuztesXueGrMMHXVq+vpYU2CVeULvjOoJWhFoNUw9Vmy+ZHS5HIsmrieUL3mpIMOWVqTsQcmvsgJ3TuKTkQp8qBladjxlmHM571FFmmhSVlbDcwON+8oaWii7BYoY4OsZn1eCAuMyFUHg+RzVPg+jqxLZnOar57eYPrPYdpwcxN3+xKh1ky6zwzrZrSddjOU/NKIeF6x+3OcHPlsL7nNGVCCqRaMbbJHf3uiqv+Brt5ARJRnMnhiU9//sAyn1jPJ/IaUEqYK2QmigFSar5bpbjqR/7+xQbuz9wlqFYjzhMLzDjmGHkXE6dcEGXYlcy6njDjwOg9zlq0cmjdrpe5FGJI1JQpMRHDSi4BaN1MbujZxsL9Eng4PrLZVXq7wZqAidBnxzl4ivSIbQ2uWQLYiraKofetsJdEsYWsKoccuE+BWeBwiFRlQXqUhlrapnjsB7RM1Lri7MV6ky8PhdRAHUZrlNGUklhCQDCgGs7OlUotAYj0w0CImekcWEKrw0ZVsm7pKoVClG6llFw0TeA4NbjJoDzWGkIIVB2pA2AiVQki7iIPFBoSVcgolPd0emi2sWPESmK3SYwqYk1FkTiuiQ8/3RFW4Xg3M/39md//wzfcXO8opvlPl5A4niZCCEzniWWZ4PJQrbWZ1PvO0/u2sDJSGTzsh4rTz5SwekF8Np8oGvx+YPPymvtTpBiwylJVg2FLqRgSVXccYuMJfDtWfnvTsRshs7LdjVy/2mHHholMpVAaKp0lag5zgizse8ttrwkJDmvhem/53beOb/oZs5xZq+C8g1wbh/UXZCKU0vYNONjsO7rRoCRRk3A3dbxfCg8hsYYVawRnFcu8cjw/sYT1soNpEqazrqExreblfsur2xFrfy1boDYsmUiDtVTV8s85FbS5bPkvWJgvm/8vzZc5X4DBv8RZn+WANp0abVG6TT/PlRjP3TxfBxC48DUbLMJfGAdNB7Kupx92hLyQiyaasUFupSKqIDlic2wAmiKkGgj1ROkXOqfx64lyzlg1UGKD05S1klJoSLm8YJMmKGESjRocSgWWmjFoqJUwJ+LaHjwxTKBh1MJvNxuiH5n6iXmu6O6KYc08HWbsqeABSvPw5hiReuRq57i6usZsX/DT5w88HY4oJfSdpZLaZKAzIUaUA+Uq6fjA46efWc6ZvAKpYI0CKRTVYo5KKUT1DfixBl45w7JxpGnl7KBUz2Gq/PS4UG1kKoq1CqbqVmdtFWtVDJcDs4hDqOS6thI/ic0zVXNbFhUQbbCmY28HvqsGnSo5H3i17/CiSLOhskXEsIbAaTmw8ULvFelSSKm0oYoix0zMhaIgkJlrIppCEqHkiGRNJaBURQt0/lJemdsy1Lt2LdQqNvZnhrwk0O3a225OrepaY1odTKxYq1GmPYCWVDgugVwXvBWM7pFc8Cq3jH/NJAq9sVy5nr42We14ydRLKmxtayrVNqOkXGDM+vIea1V7mUsxolEU7Xl4yhwPiZwCvkt03jBuIFI43FU+fj5y/zhz9+GOHJ64fWHYblyrSc+K83HicJxYlsQ0rYQ1Ng2aAkXYDIaXV1cMrkOV5iPeesXoI0oSiKPZzltFTgFQoL1ld3PF088PzGFh7DtSiWgR0IoVYYqaZVl54QK/f+l4uRWQSD96bt5c0+1akWdaMjGuUDMVwykkjmvBYJqrxGbO58JmY/j2jeemX9DrodmLtMZcBrWMIsaW6DTWkaMmSWR/u+HF2y3WV3KOPE7w09HyabWsQS4MEaAUluXMsk6t6ocmFVjr2G069oPn9c3Iq5uBzl1sb7/mcM0lXRaqzUD7jBtM6dnb9bzpL2hdv9JSv8BdGh/x+arfiuHKBe6CXObX2hpdm4PA/pWF6/lw1UrQYnDOonVmXh8J8yPeeKy/oqYZa3LbnDNfooCaqqS9Gipo7SjSU7NF1Jlxo7i+GXl/eGTTbam9oaQVkytljegqVF0gV5YC0Xf0nSfXzCoGlzM+00r/ciaX5iO0zqNNj1KKYfTstj3Hc2E3aV4XzXkYWO4n1AlUsRjVMQyZ8PSRKtf4zQ2bV9/w8+mRaf7UmAYpYnwzqccwoeQK44Tz6cjj/UfW08R6NoTFE+eAlZm+b77hBCTjqcZRa0TlSOcVL8eO2QjvUmSqnkkJ6RR4uTNc14E+JlKIOCMoM5KlkMSiXI8yBmokp0StLY6bVKOzi1It3FAMdc3U6UifCt95i+muGPqOOAsfzhMf72eUrlSvOE4rIQm7oWs6NBVxFuubw0MVgZSY1pUqYM3l9RYqOQVSjtRaMc/WPZ3pPIx9z9D1OO9YQ2B6KhxVZbkUZWpp1SEWMMrilaOkQsorygshJ9blwJIUSyoUWnOto2CLRpfA4DU6J4wonPbsrcGXiFGKJSdiyORYqFYTc0LCgtea1sulgEy5eGpzzi1UI541Fh7PM59Pj8xpoc6VbjaYWtsDwhh032OUMOw7di82mN4TaqDmSk3Cp88HDoeFNRRCLKRcLo2sLfp6e33F1W6DM22CNiqz8ZXehdbUQaXSuItVLna21G6T/aZjuBr4/P4jvhiM0u3zSovWHk8Bkyfe7DNvdoLSK6Yz3L69Yvtii3JtkZ1LJMWVWgpZMktpO5WtU7waK+QA1vPDi44Xm4yumWI6KE3GUFmB1qRYKLm5BEAhTlrB448v2L/oKHrlfM68PwmflsrTslClMvbd/+DsvXosubY8v9/aLsyxacqR17AH3dMzgh6kV33/RwEzgCBI6h61vffSlElzTERsq4cVmVVsoTUQSRAkimnPiVix9t9iWmSaZy7XKzE3zXQoGbGG43Hg7jjw7m7D7z8cORwEYzO/uf21lkxrsiaTg3NOrXSvzP7X+haNH/xau21eSry++etV89qa1hc39UgrefVCYDU1DKy5sCKyOmgEby3WRh7P/8LT6RGH5cPbvwYz6PGtWaBgJH91TkgDU2k4qmgiUqDHVIu3nvtb4eOfz+SaCEFoHRgswwp+GyNYaczB0W13tGr4fI60RegvmV0Uht6Qc2ZZFnYyYu2AMR25FgQY+pG9OTLVCrERbOYkjSSV2hKlVW5vBza7t+zvjnQ3R8Juw/G448/Ocr1cqREG05FSJsVEI1NqhpaIl4XpaeLhc2NZBtJSaPHCcevph0brLMl7ZiIlPbMdM7vthl0w3LvC82VhSZnkAzWAr8KYHO+6nou5MF8uPDsY9yMxJeZ5YpAOKxlyXrdii7EF8RnJFrN2m+XrE3FJNGNxLrALR4Yw8niemOOVx+mCcZYS9nyhcj5PbNNF83JFcM3QS0cfPN5Zcm0sTY+h4gTX1ODwkpVa0PDnktV9YJ3Bm8AQApvNQLjdEveNX+zE58+FZap0xmFsT8ekbQClsqRENYW06MPdiSGIthqk1Qq9pAWsZexHvFhsKgQsvTi2wOC1VUJSwyGMQQPWa22kGHEhrMEfmZziep95oGlXVANaY7/11NpRTgsPMfHwyn9YrO8YDwPBNbqD41QW/vHHHznnC7ttB9Xx48cnztNCbapZLkU3LmsK+/2Wm5uREPQ+FCkEV9l0WklvTE/FYMQielZbcduKEUMYHMf7I08fPzNdruzGLQ1DTpUYK2VeuO8zH/aaMme7nrsPN2zv9pjOIk4gqxi/NY0ybAUWNF/5JsBtV3CtcX8I3A0Z3zKlebAbjBQcidJESxqjvoYCNFvZHDo+/HDP7Yc92IWYCr+c4aer4zE2CoVhMHhbybFQK8xZiGvjq3HCfjfw7n7P29uB373f8+ZuxLpEbo1Sf2Nwy6qJUqZQXo4v3+YMvFhXVUf3lbRS4urXigEdlmVNGgdN1zGvRzIdoC/Fbr/Sz4owDgMfPtwzbho//vx3BFv4cP9XON+TS2KJF1K6QpsRFqTpYGvSvnYhr7+SSF11hsLhuOfd+7c8f3zEGos1BWMKvncUA8FYmoO0H8mu4+HLmedrws3aftkVR98MyxK5XK7clB3W9HjroS6UtJCj6nXFWEqdoGpfECSMa5gAh+OR/nBP2AaG44F+M7A9HOg2G+brI2IcxqpEKYRAI7NMJyQVBjfyNF14+HzmOqVVAG65ThVxambwNpPjEzmembxlqJXtxoM3XFNjWSYiCQmWa8yE68S267nd9cyxkS5nsm/U5oimUKMy19SKIWBMQGym2oVWPYZI5UozF7ANsVuaDczZkebCNS2ErrE9dPx8WvhyvvBFGnMQioUgBlsrIWeWa2KOjuACMTdyAxc6pDOkArtNh6uN8/OJKSk7LtYowdKabjNZ60A6K4x7R83CaToxL4WGwdu1eroVUipUUxGrLqGu07i/6xIxrZHEIs6TRZQwM4pBj84gS8GXAtMEoWL9ho0DR6H3hb7zlOK0ir2pPbNWo11VWKyzYEHEU5KBHOlM5WYM9O7IZl54uMxccyEZC1Y39Ebj8XLm+g8T//LTX9huA4fdlj7sOZ0apRqsU112rgVnMptt4P5+S9/rgK+tUlth8I0xJN1QV9ekVtAIsg58TKOZgpjG/m7L/Zs7Pv7zT1wvC+I1HEcKbFyj70BMxXYddx/ecHhzgxkCWI0mZNUYt5qxFlquxDTTGcuHbc+xKwx+ZLvz9LYoBp2VoKRpzXWKhRyFVpzm1trE7n7L2z/ccvt+h3G6tT9eDX85BT7Onjlnhs7iTSXFRfFkE7BhpB/0WN33gbc3I2+OPR/ue97edoQAVQQpGv7zm4ariEFW5vRbg8DLwFSdq9ZeNFFM82WolqKFb4D67tuaQ7kOVmcdWsimX6O19cV6HcYruYUyozeHA3/1w/fsdx3LPNFvdvTdDqGxzE/E+YmSzmozRbEixZSqpgI1S1t7cqTpVopEOt/xh9+/4cc2c/2Ssa5D0oQX/d6mVupmpHae50+fuV5npBlsaXQu0K1FeikuXM6WyznRuQQuUeqszbRN4RTJlfkyc31W6VEuVZ/mQZtGtzcHhmNH6Lc4GzjcfeBv/4fGT//0z0huHO7eMhxv2W1HpEYuTw9cf/rI5SnTzBHrAyyJXFV4PqeFXkaK6Tg9PVPiwjiMdHagxYXqYeeF73rP+Rw5pcg1Bv70ZaG7PoF4fv/+jt1mg+kNgy2QruASMSbKrH1mEgLgqFJoxRPsCAZSsVQs4iwNR65wfXomlYU0n5li5lLg01x5zAvVqT6xiSVVNbDIWqlexXCZM7UZUjF4cfRDj81ajX7Tj+x84PPDM6dFE6msGG0uaI7zNNN1jbHrkM5j+kq2E5GItICpho21mFpoDjqjlTLBC7230Cqu8xgyS4NmO4rR3NTBejZDx12/pZ1n0vkJK+C8o/d679iaCUZhAuccsVREMlrtZbQ/qghNyhpWrp1fVIWnhhDofMcQBrZy4XleeMqRGbX9xlzBWpoI18uF6Xrml58ftIhTAtZ5Qjcy50qpkeAbd3dHxu2oATdUrFWr7K4vDAGN40MZeBHVfraVH3n5G1Oxo+Hw/oaHjyeuzwW/ZLwUfBM2QRBXCZuRN9+/5/bdG0zvaFbWDq/11CqNRoGWaTUjNXPbjXy3HzhsI6EzDJ1gUceIa5VcEvM8ExUfQVBI0VjL8c2W9//hjs19h/WVlDLX7PjpYvh5VhzcO8fghbwsWNFam1gzod9wc9cjVLaj5+2x5/19z/fvDmx3gYqjlYCYzL+fifXf7dBamXqjU1xj2aqy8KtV9d+K/vnGxvpvs1qNUfeIklHajNnQDViw0PK6YX5VGlhr8SGwLBM//uXP/PDHHX/zH37g7njLZjwQ45np8pkaL8i6ETZTsSYr9ECGEldLpEWrQUQvDFUrM/aV7Tbw+ceZWi2bzuJahpRItVK7LYVEfXrCPWfGYeS469n6HjsXTI1QCtfTzC9/+US6XtntHM5lUtJWWm8NeZp4+HRiuQqmWNKSsBaGvpCz4pfiVE/bxDJsD/z+j4LEC9dT5Hh/z/72nn4YiPlMvUZ++fGBjx9npO7Itcc5R3CGusyAJfR7XNfRNYN0hbHvcb0mD03XiLONjcAbL/xUGl+WphbMKbM8J+Rmz3bU2nBTLuRlZpmv66nDkbPR1DDbNBqSDhv8mh0aqFVJp1R1oOS40EqkVjUzxAqxNYwkNiZjnFNME3DeaKWK1UCQujqNzpeJfa/NCMEbXM1YU9j0lrTx5Kp2RVNF63K8B0nMy5UloTXKnUG6RvVJjRXFEmtDSkbwSuDmRF0iS8zcHA8ct1t2deDLeaJIz5ISYx/ogLJcySIMFlzXVrI14I1FRCum62ol74YRb6AajezEWiWyVu6hViHXSmsWaz1VLLnBvEREDH0XiCVzyRrwjLGUXOm6gTe3B7ZjT8vw9Hzl8+MzqWbmtPB8OcGqWthut7gwUJultDXTwzjGrnHcNjq/WphYk8KEV+0sxqwlpUUhQAzb/YHD/T3z8xfSuSHOrRK6ynCz5d0Pb7l5d4sd1B6r83RdvlDlRquVkhfS3DDF8mbTcbcxbAaD6xxO1G5bYmWZEjlnailYPE08SYR+N7C/23P/xy3DjaO4hUalLPDL1fExeiYE7zLbzmDqQpWC8Y7rAnMuNDE41wjOcNwE3hwGvn9/5PZmizWQi1sXSYDfmC3wK4ur6LDNRb+Yec0x/AoTqC5WB6pzbh2+vz7if/V5rSzT+u+v+KpCEK/fG0sqjafzzF9+PmHdLb///j/Se4eRSpwfyNMFQ9FjzJp/2OyaslUzVdJ6MciqCgepAtlQypVaZ0qeyWVtkHWachQsXAsk25Drmc0SedvvCd7ha6Zzgeoaphm8aPfR5flMmh65nAy7rcdbrQ1JOTJfFj5/vDCfDdtxs3ZWNUI1iFhSvjBPEbFbnAuUnLmevlDKQrNCLInpciFPC9YWfPPMi+HHXy60pWAzKidpBkfEe0tpjTxrvN5hfwOtYUPBOEeMF+b5GSGyxXFsjY/zleYMu83ILiS8WM0+pYIzBDqsyViDRg86Ty6N2jJxzrRaWeaFXArNeIpV4iXjqGhsoLU9rRl679i1xOC/kHJSkXYIIJ5cG85UTNWNn6pCf3GOu+OB482GzWBZsqa3VRfxzuFzY1M9Q/uaVOZsYTN2DJ1oU3B05GYoMoGLIIZcDVMpHDd79sOevCSup2dKnMl5RsqGwRucBL1GzMCnL58hRoZNjxODpAlrwfpCXNT6WZ3Q9yrCV3trpbWrSpusQm6Ix1qHGF0MjPRIcSrLEktqjc+XM+d5JtiOWlQtgTcM3lOtw3eCSIaauTseCG7AmEdOlwK5YUhYqxbqmjWesrbP1GK4vT2y3/YEa9h3iV1vCMbpbbrmMb/U3r8sXEaUd1EsVnDecf/untOnE8vpumZXzOzfDtz94S3HN0dc7zUEaVWTa96DZoa8mJBqypTkGU3gfhT2fWUYrFbe50ZaInHKtAydeLLAUqBZodsPvP3uLYfvRvzoQBYN8kmN50X4eTKcq0UM7IaAA1KOWB+YUuPhPDHNBeqLzjdws/N8eLvj7d1OU7hWgr9Zs/as/UZCC74WDr4OvxUa+Iq9age4FXVkgEqyvNfqhJQSZR3IIrJeTLJ6p/VpLt9oYO3aslkrq5j/RXZlMG5H6O/06C5RcctStC3UaVMlayfRC1aL0YHaavo66CtIMUitCIvGxMUr5yVDcmxaww5OZTgGllYIJMy20ZmqJJno0zzWoqWGTptd81JpqdLSQp0XDoctzlZSnKhFGLq9Bq1QscZjXNXjkcnUFomzx7pKGDPL9Mynn/+V56dfKGVgmeGRRygJFwzGBk7nmTRl8nVmDA5qIy8FbzKVQEoXUkosaaFzZg1uNoSgEjhjCmmesa3jJniOcuXce3LN1EGrw51zelwtBus7gjigfGWPnabPt7kxzydtrnUeTEc2nlQaFbverJYlNQ1iMYa+67nZb4lTJpuGuIa3C50I3ndIEVostKjyqs1mTxh6xr2ldpFTKsylkWqkUam+4gYHzWKMZm52Xcd27Bh7h9hGEsPz85UcI4ZMNY0ohpvNkd/98QfuNzseP3/mC4W8NEoEciEvM1hPj9YdjVbIaaGTwBA8GwODK+TFa1QdjXG0bEb1+kPW428tuhSidmSL4IOjSKYiNHFYb7EYaqqUHJlbYjKV3FmKNOYM1VrG3RYbBmKqlHRhSVceHz8T/IbPX65Mk95z3kPXBb2ec2JeIp8+f+R8vvI3xnPc7Rk8HMfM6K3mHxB5rWxq7fWU+XIvW1k7xpyhFtjcBA7vOx54Zug9/fbA4f2R8f5G1SO8hHrrFlzW28isXEtrQk6Glj03g+duUxk6wfueUiHOMylqqplzlhQn1RX3Izcf7tm9v+Vwv8eFQqkFqRVqYVrgzyfHw2JIVUPy6zq7jHfUKlyuC9fYaFWJyOPWcxgtH+63fPfuyDh6RCo5V1KrxALTrLDEbxquL0f7Wis5F5yzvzrm51zX4GaDsYaKVv82fh1HqEedpoG6Vp906tVFt13zkvYrr39m5AUs1qFtVDqHl6K9UC0pK5kbqRbcug3XVtanq1Ep1gsUgJIHNA2zqC0jJIQFaiXlwjWCSZnilbyzZFzNSJrpvcfuRCs5xLIbD3jvSJtCjhOtZZY5IRi889hiwFnmU6GZRVPaq0rJnBWW+YrD0Q0GJFHblZK0tjiHRDQTrUwImel6YblGpBbiZHl+PlNt4vb+yOXhCeZMZwMhqCvHOAidx/WGmK64TtiMjpzPiPHU1uNsrw/PJOQSsKnxphO42/F5MfxyWThJI3pP6AclH0rT3NwaaSRtcchRj/Cm0uqC2KTHMLFUhKUIsay42gopxSKrQQXGEPhwe097yjxOJw3+sI3OWbwRcnFUa/CjYz9u2e+29MOGbtOR/YJdhFM6MadISY0WFB+lVSULXQNXmdOEiGXcbhERlnkhGEMLPc9PjXjNdDXy6csD548PXB+fNbM3qKW2s06bAAq4qoaWm2EgJpC4YKwwbgPeAsUSgsXbQt9VfNDW4latkkbo7hZjJqZE6A0+9GA8TVa7d0uraw4GsRw2HSV56HrcKNix43o5U41eZ8Y2xDtKnvly+kIrM798zsyTMAyeznZsuoD1MNqiIfNTZpkaNSVcLWy9cLMVgl0fhPIV6oO6Knna6rDU97S8SC5Dw5jG/e+O7G88Xe8Io8P0HeJX4qmsGuj1OG1QHBfAiMdKR82FwQ0cdo7dRjuzSmosMQOG0A/U1LhMV5otOlA/vGP7/g6zU5ebZFblRWYpwqcr/Hi2lCL0xjKnyPMl0/WBvnfKN1wzxgS2A+xHw2E03O093707ctj3WKf23cfrM49PiYfHxMPDM+fL828briqNeGHDNNfQrB063utRphTNAWjtxcmhR4dSi0anta9V2W29rOBFM6tmAxHNLBD0eKyKZUurUMtKeAkIFa+GbLWjtkJKVyp5/Zyk/zR0cwGqKFguRhs31W5RMaZQa1pNEAaxI9P8hTpn3m8OeO/ppWBypiahDwMhBKgL12XGSsB7j+scl4thWYSYDaY5WtTEjLDm3ooXJGdqrtSoXvxSsnazl4xthVIceRlxfSMvC5ETtJndZsPl+JYfpzMlZc7nyLwIbghcLpHpMlNzQnxHrJV+O2I7Q2qJ4A2hC2w2ntBBJdNE60xyKtRqOD/D559O9B7GfeMPxx23bAmL48v5wtIPVN8jccEbj/c9UosOapkRW5mnM3O8qvbROhqeVD25CSk3Sm2IKbSmdkyxWqZo1xohay11O2JbonmPD56WDKZYhuDxzrPZjIzjSN97+rDFdZ7iIr53hIvjSc5MkrU/atZIRHFQxTCXhbwsPE+NMS34fsA44eaw47k1nh4WWm7Eeebnn35mU4QgBt855ljxxuHRbUBKg5TY9jsO44Z5tpAjvYMuWEJw1BYZraPz4ENEJGFtQLynFKFUPVG59Y4wFkotIHZl6F+4CLBiGSzsvONSCpmGCZbQbxC3dmuZitaAq6//ssykyZLXI7ClEmzH4Af8xiAB3VTEcT1Fxt7S2cjYwdivQURNwFREnN7bL60jdoX6almhQsjk9ZYtDDcDm2MPUmmmraSVhjzpA0NPuq2+KIu8QnbWYK1WGnXBM+zUpFCLo2ZNNIslEXPFGsfw/sDufsf+eKDfj5QAxUYoau/1thErnBbDz2fDlB3OGnIR/vSnT/zrL8/88Y9vubvd8nRJzKnROc+ud4whsR0Mv3t/y9s3B1wwxJL55eEL/9v/+X/xT//yyONT5jJNmv37W4bry/ZprVvhgK8Rgi8KAmutDopSXp0mLyaA0gqlFe181+L39cgOX7dUJUasNeswfqmO0UFdV4lGkabyqgZCw5pGKwutzVjRZ6j+uX3tpmd9YxF5kQwqkC51BeMFaQpO51R5Pn1BaseSNUDaeEsgsBTR1Kzesh0CoYv4bsb4Qm2e8/TI8ylB3bDrNevRAXEpxJrwQ8FIQopoEhGw3d4g1oB9xARZzRqVWhJxvqjqwVRiKvTDnm4sPE4XUq2E3hH6wDwLcxKttbZXsJ5iA+dpJs9n7trIfb/FuoC1acX4LK0a5mumtkxpFhc6bChUX3AD3HiLlD3WNawr1LLQTmeyXJF+g8VRq9XW2ZrJOdGK0Jqn1ECsHVNWdwtNlLSh0pz2Wokm9ACa/mRbpPJ0DQAAIABJREFUYjcMVGO5lkqrHuc3bDc9223POAwMmw1+td52LmCcUJhZcsfoA+468On6wByvGnQdgj7gVyu9iNqhT3HCNMUefd/jBod1GSMJZ3sCSkptO2EYDOfLRCrgbcXEF/eW9l2dnp7IcWLsAyUX8uA53B7pxsByOdEHoQsRsUaHT1NozSotRAgdg3FUvArWm26HWvRqNbJPKtY6RrdhWxZORW/m0jI2CFIM1EqudT1jC3mVonWDJbZFVQkErDWE0NF8wwTlNqSBtRkrkV0n9CYhrdAIWFEVSAPUIQkYUVhLVlL4BSpsaxtCe3FxrYSYApOrJVUhtbZ+rKBhMGL0AeM8ON/wovflfBXi9Eyri1a69I7t3Z6bu3t2b4/4cY1VNIBo2Exb7cONzFIsn6+Nh6nTRgYDl+nK0+nC9XLiehkx3vJ41aqf40bY9obgLB/u93z3/pZh8OSauF4m/vLjX/j7//uf+JefItX0+juV36xzfbGq6nb3UkbYWiPG+Iq//tvMVtWytlesVuURX+u2QV9sYd1SX9UD5fW48ZKQ1daEHoNKqKx1mnHQEqVO5DTTSsQwYI1X90nTBke17zZo2mZA1Te6UlchtFe7Y8uMA2xvtpyfYC5wiQ1bK9I65qWypEzfGbrOYI3Bhopx6lN/82bEmcpyFoJJDEHoguC9kDSEic4P2CosOVKbBg8b0xBnFEOyHlrl6eERsZbDYUPKiWXKlGygJXKtKlwumdND43KB+SJYt6UPG6zreLpGHi8XQpm0o6nzDL1X/NF4rA20pvmvhQvdAPvDd8w18mm6YrDcSeVgCrIxuBAx+Qu5PVHqzDQlDIFcigYkSyYXWFKkZqGK0IxDnMWveam5ZHVPrXZPxCHWU5uQa2HOmU+nJz6enllq47D7wA+/e8vdzYbN2NP3vTa9eo8z6gKyTllbH6vWmPSB1lU+Py5MS1aSqFi9Fq1gTKCUwrLMTEl/T9MC4gybbYddKl3J7Gzgu+OO0VUciVbh0ykxN0+zFSsJ0wm1LZR0wbSKE8297UfP3bsDnQv88mPGMuGtwYRAw5DiWvKJSu9MGBBjkapBK3qqKqopFUNd7xcxgimOjkhsGoySnWhq2KQ8g8AraWvEKVbuKzVo1kGpnlQSshiCD7jQqd3UFkKwbH3hJoCrUd8/azCiLLzeQ1lPfhWF8nhZVFa8dB20shLdUu0q39IHsEhbYb6y+oVWe6apKrd02vZ79QtxrszPkFrGhMbmuGF/d2Bzt6ffjoShg2B1M6Yof0FFqjZeCGqVfrgKPz8bEj3BWVJaOD1fNfqyN5r1kMH4wLa3bLpGcInbfc/vvrvjsB8RU6nFkZaF8+lEyg7fBapzSGnEufy24frCDOqQzSsz9lWN/6329YXd1+Dh/3fM4EuT7Nfhq55vY/RH0NStdRjy4tTSDdMa3W6NVblGTpHqEst0Zb6eoDaGMOL8QM4F0OixWtYB2xSiWO916ho5R3PrxZw0bHncUC6F57ny3FWcdMSlsMyJ/cZSRLWEsporpAklzQqsz43reYIgbMIGFzzdKHTWU6WjZUubI95DLI4YwdQFKUAzJJeQ3GHF0cjEslCqZpg6L2Asf/75kTh1bIct17lxPl2ppWmSfOnohw0bb7G24mLC14kyOc7P2sTbNY+vlc5bxDViTipxsnDKhn9+SGR7Zjo23nrHThqNiSk+U6tGusWa6PwWVQ87SjVMMTPHonpNoz+vMZW6JpLFlKhUqkBtDnEDTRxzWbguV85z5MvlSjQFrNBvPbd3e477HcF5ur7HdR3GapCQNRbvDSYXGj3NNsIcCQK3xyPTErleI3FZyKIWxdYabS3sc8azHfbMk5YVdq6x2wQ2yXE/BA4bz8ZXpuvMeYk8T9B3FkKjLFcOtmfsHMPNBiuF3SgcD4Gbd7fcHDekmAlBdOgZ0eVhHSaqYsnrMdis3IBRrz6ilUbrtW+N9lGVnFguMzUumjwwzwz7keYdl7qQYkaqkkUtVyyWYkBcxnXqolvyxPPlhCuRjd3ixg6xlspMsI2bAXauKB+x9kXxMiib1vVIXY0Ga/OsEU2PozVayesABlb1i1mP/CvFvkJ75pVw5jUFr+I6w+H+wOU58vx5xvuO/eHI/n5kc9cT9g4bPEYcNKFQEbtq4ZsuX7W9iMcq12z4+bnwvCh3k3NiWTT4+njYcbQHrO+4FhDjGILDu0YfDO/uDtwdt/i1E6xZRxd69ts7dtvIw/VEa5H9bkf690Ox/vub6zrq1iH16+HammKjX1OtNPz35f9/zXWtWvb2K+WBQaM5FHZoqF0S0KOCNJw1iLhXqMFYS22NmCLeRqbrlZIitRjiUrC26tPUoBcoWsWgP5NmO75AErohRw0eKYkvp8IvX67Ms2NnPKel6uaVGl3fMWwNc5rABvr1GChYKpncHOc8k6UpudBb3KDymNIyrSowH0lUMiJJnWTJIK2jOiHGiO0avheSFC7XMykpWdjE8/Np5iEGYvQ072ltphDJUsgNTLwyZEfnAoNtuC6o4NoPXGPCziqYrjWqO60VXOmoIkzTmYbKqn6aZpZPiXA88nbTIV71l7b0CAmqIM5gMJgsxFRZom4A4rxuKDVRW6HmtKpJHLkUrkuhVEt1hnO58nj9whIXmvFI8Iy9wxg4HjbsthvGfoMxaxyfCzpQBaxZtcBkPBkT4HF+4E9//lfc0HO8ecOmMyzXC+fLhZgSuRUEQyHg6Qhly9PzFZkzO7FsrWFnHdveUMpMtIbnDJ+ulUuxqwxuxmXD3bjhuB3YDge8E/phgXaiTRc+/8OJ2NaISmtYS4ZQaN/oNW2d6lqr3lsas9EIwQGWJWZKrQRvqQJ5SrQ5EqQROs85JfLzBTN0jN7Txsbj82mFZxq1KcSArbhOwFnSsnB6+pn67LjjLf2mx3f6eu47y91oGXwF6xDnEGN1TtaCtLLyFprdSlUOpFkoFFrRrVFoK9+h6WuNurq+dDtVs9FKYol5lWNBQ6xlOGy4/31je1fYbm8YNlvcYKHLVFv0wUTRsCPM6wMLRId400UwN+HLFT5dDBlHqZmYIg9Pz8RcON4ciKWqzLKC95ZgLcHD+zcHfv/9O8ahXwk9PXX3/Y7b41vujmd+/PxAqwVrGib8+yP0/1stwIonvD51lBx6+SV0C9WtSIfti3b1m/HcXp5Ov24j0POFvrntReax4qYgYNZSPITa6ipK121UYYnMPF80OKR6csrkrHmS+vEvD4RvttdWX38WyGoMYKZIh62WOiemubCEjmnJTK1x6BxDqBiJTFGF2NuxZ9NttPlT4CqBOTRqMSQPth9wwYIseiSLE3GekLggEleNY8DOHTVvWXJkHATrLLlGEgtLafz4+ETpHEvp+dO1kvc3nNPC9csDLs4YqXSbwG43EsTiOyHnpCC7c5xzY14Kx60+z0vJ2AbTJVHTgjFoyZxc8LZx0zkepsb16ZkvItyM7/Coldd0CXymZA3eWeLCdYrE6Ci1R2xVSySNHDO1FUqtGOsRF3i8Lvz0sJDahHGZ4jI4x9iPlJXE8Sax8Z7b7ZHNMNL3PWa92Z23eN/WB62WD6o5wGHFUEnEvPD8NBOz8Ps33/HDX/0tOUYeHh95Oj1xul4ZaEznxC//8sjHn57oRDjujxytoZPGPD1yvhbu7t9Tuj0mTDhjaMZzXSLH0GtvvW28uTnQbTokJC6XgefnieVywTrY7Byd0/qTilc+wlTEFaxDixxTo1TdDJ2VNWlOt74lLipYj5F4nbW0MgSaNcRrZLoapsdINEIJuj2mWqGoJfc1v8qgoUdOyBSezxeeHr9wOGzY2w2daF34frQYVxBnwDndKldSWkSXrNLANmhVE71EHJmkxBs90pxW/rAuNKZg162X19OsdpgZcZp4RqG2hYpgBsvxwy2tqVW3NYvYSl2JMVmD+4XVCdrW7Zesi1nTh8ElGX4+NaZsyCWTauM0TTxfJ3IRsFBaI5VGzJWxd/TBcdx2fPf2jpvDRuuLWLHhBs57hmFgt+vpfGE+zcyTY7e9+W3D9ZV9elFkUF//W9YzdltjCXkdouu+u+IxOmC/5rR+2xJrzFfIQcFuNAugttekcFk3YCNmTQ2KK3CtBXSC0KRSWqK+/jovBFZb8wwqpepDQF5mK4VaI0UKVQqbjeX370fap8jTnNhIz8YpxNCK6v3u3+3JKTGdZuZ5JudIGx1nDCfrWIgEE6iibKn+/IU8L8zPC51Uut6Q/cBsB6QaWn6mxEjIjtwSYh3W9OQ088lkPl1nxHrifo/rPBIjz5+vdLFpFCB6kfrQMa+YckqJpQhLM9TYGJPlVBbionF1xmhBnrcdJUXEqef+zdAjNXDuhE1vaEQs3foAtzSEYoWUM+dl4XRZKHUE4zUcJyVSVPVIbooPi2/E68LH5ysRQ7fdMm57thtL3/WEfiQDMU2YuLALA+/uvmccBlWk+ADWrO2pSlgaYzCr1thKY2mVtJpAxAWMsWy6kd9/+APbccu8RE7nE49PDzw8PPL45Ymtf2KoA/X5woHA3WAZAkSEayqASvre3Ab8xTCljhIXXLDMaebTxxO+zNx/94Ztv8X0N/z0SyZly7udxfisrQnWrdkB5vUequ2rMaOlleVvRfu/xBG8fYXElkkdRiF4hEaaF0JukAwlVh6nZ2In+JsB0/R1p7VXG7rYDiPacrr3+nqXVDk/P0BL7JwluC0+oLIu8xX2Uufky70vSG200jDNIlZtqKZmhIxBs0/bquyhZSUbUAIZ0SB6FR58dX2qFd3yGjvqZc3KNavtdl2RG2u+gd7fL/DCt1ursJCa8OXieJx0gIqBeY58/PLINCeM9doBlwupCrUUyNC7gbe3e97c7dUsgOLXOj8yxjg2mz03x1tu9jdcr4maC91v3lxfQq3XV1g31HW6GrXCNV6Y3/r6Jrx87rdtAt/+2QtkoIMWvpnY+qKhQn8FyfWUYT1gM+JUAlJLpNWksp6m7GkpBec1tyDXspICQs6VHA21uBUX0hyEWnRgCIW7nfA//+cjtz+f+cd/ODNfE3MZWGqlM4auH9hsOqztmbue+TIDieItMQoXa5mtZa6WeTHkTlO2DFaNDaXoa9YGLB2RhTAUulawocP2Aest3TiCcYjr2LXMT6eZEnps7zDzQrcrlJuOem5khJjhfI1EhLhUfM70FMQ4ailcr4VgLGasCJklnrG2MPQDqSWM1yxSz8iu3zEOhrmMtLZgRTXAGShZq3bmCKdL4nKBaQnk4hAazurHzHMm18ZcCnOrJJpixyGwPezZHQ9sNx2boaP3G0LX4TuHsQaHoTOOfrtT0sW5NcjEYKxaRe1Luy8FS8agIcvWQt93zNWy70e+e/OB/XjDZtix3zhuDpUPbyvXyzOn8ydOPzzyj7d/5r/9178nLIWNN2z7ggmWKcOfH75weXhiu92AHUnXjCdp5q/TJpaSLqTFs1wy/8dfzvzv//yZN1v47v6A6wMt6QO9tK/h8rVAexGxt/aqEccoXl3LuniI0Y4tY/G9Yeh6clp5igrPc6JcZ1qshM1Gt8g8KxxgZLUnW63Xfr3vGuPoSEthma5M1yttE2hlxLgdxneIX8v7ii45rCdJaXr/xCRcY9P2DW/pXcXZRSV+9K8FidpPVVVy1TSWtKGktnGyqieKwg4AdoUK1s+vrSIvcAJWYzfXZU/WMsQX2AGqNpO0wiV2fHw2qj8WIdfE0/nC58cT4HC+kdMC1qk5ocxsQ8ebw4bv3t2w33is1d60l5P4S9Tq0I28v3/Pf/rrjHc958uVd/f3v224vgy+X7e4rpO2vWy1L2/AN/js+nHtm1iD14vrtWbbfgMT6JA22F99LxHLyzjXHFjN7KRVcp7JWcOZgVVHp5W+ta56QVHnSMmGkg0pNsqiOKzivI5WZ2gznSzc95bu+w7XKn/6u4USe1Io+K7gDLjSGLqB/jAg+xFYeFgqtXhmn0gWrnPl6XShD5md0ZaDzbiFpVFiIWfDJgiDX/AN+rBBhpFTnFVgXSvn8xN+MGxNj6TCLBfG0PDOErYC954lZMiCN5ZqC+d0Jc2FLRXXWZzXjamUNYvTWBqZZqCZxpwTtlU66yArRmfzrKRRyzQKWTT6MVZhmhLzrLUln0+J56lxXRK1VnpXGZxRCKRotc5piUxNQ0W2w4bdbsv2MLJ7kVaFHu86QvCE4PGhw7mg5IQ1WK+ZAGItxlqM1e4pazUVXxnorOn9WbXLfejpbcfv37zjw9s3+j1sh5iANUKwosy+scTHxNOnC5cvz2y8wwyCM4KTgu8N5XZgZzPD2DOzwbWZUxzx1rEZhMPWcrMTRrdwebryy6cLgxPe7wKjtVg6jHPqts6amWEQXAjKNre1kiYlvfatFnalXEixrPyAZtO6lchtrRGCp/OOeS7ISSjFEq+NYdvhfKHmCbEF65p2SbW68kkGDaJWcpmq98FuYzlsnb53TUMFBVbp1Hq7NyFly5eT8A9/fuQf/vLAHBPvDgN/+8OR7990BKedbBhLpSDioDmaVG1uZtW2Coi8aONVq45R+FDWRhOMUEuGumrk1wS79nIabl+x2kZev1ZmSo2PJ+HhksjFaZ34lPj45ZkpqkMtl/xqtZa2cBgN72+3/O79LW/uNoSA1imtf39N+NOHyd3xFvAM3cjj0xP3b9/9tuH6MhR/jZX+eli+ztcX/HQdsd/suK8f++uv+TUIG+pqf+ObX0afHK1qjYwAUgvq5WpMcdaU+bYWG4oSFsJLqIReFBqwEHBWSKWxXCdqKbgA1qm5oDaQYrDV0DvHu7uOp92VdDLUWrA2UeszMXWMWMVLvR6n5tEDibJofXeOjfN0ZTsXNqMnZ6E1h+s6Ul2YnAVX1fmzBCoB5wxeLF0fSDnCqnf0FqRUylyxwWBtx2LOXNpVh0stDKFHvJBnTS9yojbiVjNd0MCVsTcYs5BSVXG3WGirvXIxsBScjRjJCBDTAqbivAEHz0vhfM2cLo2H58THEzzNmWueaU3YdZFD3zEGfahdY+FxmilO2G62jNst2/2O7XakC4Fge4IfCJ0OVhc8zgWscRhpuBBea6iNta85wqo4AWsrmQWh0GrBG9j2PTebPTfHO/7mhz9w3G0I7qVcDyAittL5iAlVTR2Xib01HIKw6UX7o1xHE4NPF+43nmFonHPmfuixuayVP56UdFve9IY0Vz7cDPgm3NiCXaB5kKAPOayjNu0Kcd4Tug6oxLSocqaix/xaKCXpSatUvNU0LDGOaZ6Jy8KCOp1SroR+pM0zv3w8sTWG8abHOzA2Ym1BRIdpLVoGqiyUKibENqxUbM30zmFMJpeI5ICgx2VZNdG5OH58aPyXv/vEf/m7n/j5OVJrYusavzws/C//0+/4w7uB4OpK1wtNo8cpxNU6pCQZktX0U/TEa9aT7LofK+Hc6qs5TEfES2npi+yrrDCKarWlQqqRxwt8fKpM2dDQWMdlKcxzRWQgZc1EEDPSSsW7wtubPd+/veP92wPjYLC2KI+Usp6em6etTjIBet9zuzeMfc8Sl1fp2f/v4eqce8Utvx203w7bb4/1bdWvvTDyL5kB+rlfsddvXV9t3XoFg3Me792rbq6sW6q1ASdCsF4DV5D1qaZuo9ocpuqxtTbRC/nlLXWeVqFUsFKoxbHMM7lMhKFgvJIKRnqc2+DE0g+RsDlxOZ1p4jHS8F5lSaVeMUa7paobtFXVTniJ6rpsFR8codchvMyV0+MZSiY5z2kYaLZxyIb71lFJeNdzd/uWaiwff/rEeaqM0tN3QmcyeSlMdUFi5nQ68fR0Rh4qe9aix8ETxOO8hZyYlsx2Y/GhEfpVGoXR3Mtcidnre2UqxuS1PqdqZXkzXM/KDo/bjmWBx+eZpTi+nC3/+hB5miJLqTreSuUaJ5aYOQwdzjlyA3yHsdD3I5vtlnEz0oWBzm/owoB3nhB0c5W1Msi7QNcFXHBY67BOYyntCiHpRiuI0YLE1hJ+xei+f/OezXjD/nDk7e09g9cbxZhIbQutTlQSRiaCi3y4d/yPf/2OT3ViJLLptT7HiHYuxekR2RSK81yeMkw7fIaYC9cmnEriPBZubm447nr+WC+U64RNUOakGbbOY0T1qPYlsq815uuM81Yde6UqUdu+bmANS22O3DJeNCCnCz1xScwx0opqtpcpkqLm3Z7/9IljOnD/4Ujne2o561Zs1GqeU1nhMJU2Wm9x4uiAQMRgcOKx1azMvmKuuTg+nYT/+t++8L/+3Wc+XRwTbs3xsPz9nypj94Xe3/HhrccbhQJaVSy0rti1weHE6ZbKC1T4shB5KlkzKRBoRqVlAhihmbW1t7XVtt5AKrSMaXrPLbnx6dnx+VKJ1UIr5Na0aj3r5xur9S+CY+wdx0PH29sbvn9/z34bEMnkWsjrQ67hNesBnStKkiuZ2nuHpZFy+n9Ie7Mfy67svPO3xzPceyMiJw5FssqyLRnobqABv/n/f+mXtluypCqVVKyBQzKZQwx3OOfsYe1+WPsmaaFtAewqEImqZEYG4p6z9tprfd/v+2XF9Zo4oIWzffz154uqXjo/Fthr+66hhV6dWfwEfbhqX6/qAmPB2UhwAzFGjNETXLrJQESvAd4FvI3U2qEupmGMbqWlddBEKzQ0JbbahnGWYEZMq2ypYprF9lC2XAWKpic0GzVug6A6zQjDlEmx4t2IaYOerBYdOTT6vFnhyQdnmY3jIoViDCHuGKdIs7qkK8XTsiV7xzqOpGBol0dufSEGg6pNLdUEcph4czpzuwg30XI7R35MGzWBTYWyFpyNDDvPTMO7wmQdk1XATdZvEu8rtZ1YN8UoOirRNpybSMnpSwq44DE2UbFUPK00LgvUbrVswWEYaWK5LJk1KyhYTNO8MaPIwK0Jqan3XkzD+GsulGMcZ8a4YwwTY5zVNhy8Jm36oOYAF4heLcW2Q9Rdn0M6+9NoQM9lXYY26zAkovM8O0zc3Y34EBi8w9uGMQm6rNwaXboYeyHXE9vpHbE+8PkLz+wcuVwouWCtEJzjy1/dYIYVGye2NHM6Ry5lwTbdTBvgsjXuHxfu9sLBZ2TnqMmT0UIN6LzZD1jrPnI4WtMtea2tvwea2ea69FBfar1lVGvIFEqrTPOeefZcnhYKK6OHaFdMrVyWzFIKuQjPn88q6+rR4A79eYgIYooucZtlNobb0TL6ijUDzt5gCUhbuypIOK2O//71E3//9T2npIYC12f6mIFLbXz9/SMvn8Ptsy/Z2YwHbNOxA1aNI6bpdb9ftlXKh6h6hu6usg2xhlYdmKa6X0NPbzW02qCbBq4KINM8jcZxi7w/O7ZGd7plSm2s20ZKihx13lGrELzh9rDnxbNbPnv1klcvbxmijkJyrUr+a9CwVBzSrOIipVJKRTrqUEpVLf0vKa4/Ja/+NJD/+RX/ao/V5ZT7aBa4zlRpOqtw7nrNr/0EcRhrEcmIFKzxH/O3SvcRVxH9weKIYSD6ADhSquRcEEm6bdUWDDpoF1EikoJ+a3+xwFKwpqosJYy6yTa6iGnOAuphbqIykee3N/jzhWjBeo18BoMzezyGWhZ1djnL8zAwukzGsghcciObSAobs2Rud47zg7AtF9qmWVbWNM18j442zJxa4PFU+csl83oTYnjG8+b58vnMQ7pnNZaVtScSQLUCwTEES3OVZC9YrGL37IQdRmrJSM268Wuld/Shv9B6X3De40ZPbZVTEiRDEk/Dsl0q2AxYlmRY+iJDXVa6rW1N1RabsSQM8aqDtdLpaJEYJoKb8G7EO4/3+v97H9WW6JUfEJzyKrSIasTGFUHpvPuof6Zvsb0PGBIfHXw+YF3AGbBGpWEa+6MLM5rDiOfxeOHhxzfU5S2DqxjrupSnEl3GWsc0G8J+JMvM4zzRZsO2wlYS+9mzGywx3tCM7aGBkWVr+GwZjKNFIOrPwlhPE0u/2uC9o5SfR9XrI2ydw/qJIp4lX6jVEryOunIpOhNuBlMq3sA8BoZTIzhlp16eFratcLyfubmNDHvPOET2uwkfDNKSznTRH83YKrdRiE5wwePCANXRiqAIzMYff2j80zcb5zVQW4OyYmtRGIsviHV8WBu/++bIrz5f+c1nDm8VEWntVRKm0TWtZZptvVtV/XsjodaCgBWF59dWaKYv/EQ7TbWz05u42sdiWmO2Irw7Bh6To5hKEv2aWYTT+YmUzjSU6+Bs42Z/YJ4DN4eJV6+esduNfTx43RNdsacRqVZtyEVJWzqzLZSsiRW5/MLiel0+XQvt1SxwHQ/8HD/40++h2zy0wxSRjhQDMP2lChhjyNmQqka/0LFj141wQ8cHMQwEP9BKZd1yF/8LXA0HpnMnW0H/Qosl6slrrpKairXSM54q0VlcnfHe4Y2l1oKQkJZoEvEMvLjdY5Yz7Xwkm5lqA8ZqpnlOK3W7MARLmCIRHdyLjxxb4f058+JxxZGw6YlcTmzN4ezILq+YS2IYwFjLw5pI5cS5Fd6dCt8+nTifGncifDLDi8OOf//C8s/3H7hfFpQY1pC24dqAtzOrLVRXGWnsmuZZZVEI8vXQM6YqQ6GoNI0+jzPWEONAkawW4S5VWbaNWlRJAXBOliRKMHM9i8lZ1GMuUHNlaxvGBVx/Duy1ajSLtbF3qgr9CWH4WFi9973o+l5Ee+3+eLDbfiBrNySi2s3gLE10Vq+6y6IOLnOdg12LccGaqEulNhDMHlcavs9hm1V6mkHncDFabGw0CxXBupVmC0lWns6LpjuMI6lm7Hjg9svfUORMOx1x3XFXbNYQKDtgGMhZoTW1FrZt6Zlz2pUbKj4GcAPGjlpsmyO6QHRRdbbOkrczeTspfyJtbFktpSJQaqVicRIom+NybqRcqLNnCpYxht7gbFhgqJXnofHyEBjHoAvMetJ31zVKsbx9EP7hj4+8f7yQxVGkQAs4HxGriQYbkdIm3j0mfvjr5mvjAAAgAElEQVTwxK9eHRiMWnKd7VpUqfyE6bS02m84TS3QtlmcjcRgsTWBpK4QcZjW3Zr9WTJULdJNRw21GZ5Wy7ujY8WTRMiin/2aE8t2ppmm3wKNcRq4PRzYzzMvn9/x7E4paaU6anNIX4TX5qjZIwWkbJScSbVohl0u1KrxQfl/zsr+t3Wu/3oZ9fF3jLJXXQ8hVDqW/pmPnVFTXaJzCotutZ9a5idGAd31ZdCXKwbtoqQ11Tlax7auSNWoBgMEZ6imJxpgVENnPLVALV3iJXpqNrp8xTv8EBn34Eshl4oY6Q6Tqpv0VjuJ3hJCYjpUkqnIIFSruUpbXliOqx4WsyfGggeGmjBSSMZw3iCthnYTeZTIfTlwL5boGnfrwn6DfXhGGHYsTx/45v09aXhBEo+TgK0L4k4U9gS543YMlMs3kBM75xS8MXvufGGaKn7es4mDU6d8WUMpK0gjDB2m4ydEMpiEMRVf1QYsVWE4zlgNKSyNVAuXUshFt7lFFDZcqycYcMFhRTC14sRgbSDgiNaCEXKuHy2QpRbEVoxvWEcnqqnMyl/NAd7hndPu8mdWaWtch/pcb0UdgEKX91yVLNg+lMq6KDWxv4h0CZAg/TkotZEvFZPAe6c3qRi5ixOSGo6MdYXgrca4tMTLOXLaVdYZRpk5TDM3tzPRnYkh0dqG1ML+MOCdIycF+oh15NJBQgjeW4ZhUoJaSkjT96FKoxmHMV6f2JaIQZjHyDxEnHN4WzmlTC4rxigEfd0yW7Gc00ZGlVO6NzPUYglhINo9VjzkRhgtw7TD1o2DrHx5O/Dq7pboD6wnkPrEvNObxWV1/O6bjW/fn9iKcE7aqQUCbhioRqj1ojcXPFsRns5nSpuR3lwhVpseBGMNWRy5BC6XxONj4bg0nk5nRCrPDju+/HTPi70nmII6OK2yRsWgC3OHaVoobW00k0gSuL94jiWSmuGyLWo4ksJlTZwvF7YEhsput+fFy2dM447dNPHJ8wO7Ud0C0rw62wSKNLI0UkmUVMlppaTCtlpSEYpsiDRKhf9F4/pvz1y1+/g5l1W7We+vV3k+xmJfu1cV3qqRwIY+Q606DC5Ff3BXNuy1u3EuYI3OQ5toEmQtFaGRU8E7JWcFb2jd5qfXC/koz1DzumbzGPNTV1NpiLHY0TP5iZwKNidySWRZVPYiOoIw1tI6pCJOI2kz5KZdhbQzy3qiVk3dFKtBbLua+TwaXg+N94tlzfC0Celp5cNaeXcJnJpjqCv/we75crcnBIdIYx4CX7yMXEzkvBZGHCKRSyksxXOTRyaf+GSeKJcLw6wuEVc3Dr4qqGb2bHYi+UrIjdkb8nKkrBeC9YzDhB8HchZKNirLSo6aRaEqKVNNVZdb1rFLLULNlSaZTaBWxxg8g7FUwBcIxoAJGk1dRRGP1P41VUFcUkFKwdqG8w4XItYFrPNY73syQUdO9luL8x7nzcfnzBhHM9ebE70Dosvw+uFsBWP1RVattVpJNXdVu33ISC3c//Ce92/vmQdlLDTbCMHhhgmqWpO1i1aZ2s4IXx4CgzgexhXrGs9vIvvdwDg2yuWCc2oPxQV8CNSqS6EqmVoTCBQx6jrqXX3OmdZKV0VETHC0VnB2ZRgFa5e+1LU4IwwhkFoiF4OI5bIJPz6uXIrQrMc1PUxqH//Mw47bww270TIOwjQJo8nsWuLTaeLz57fc3t5BG1lOG1Iyow/k4vj+beWPPybEBcRUxOjvBRexQyRLwjZHbaJ24SqsmyeXhgRwbugw7AYWlgI/3hdevz3x+s0Db99lHs6W03oh1RP7yfGf/+Zz/sv//jmf3EZNDzadFvexo/tpiU3LiMncb4Y3R8t5q7y9P/OXb75DpOC9vscp62r7cBh4+ckz5t2E855ntzM3OwuyIeKRpsnCpcBSKmtOrGtiOS9s25laKzVFtReLWu4Linz8RcX1XycQtO4XuMqvdPZ5xRJqgsDPFQPXqBcVCuuV0vugXFSkJ8bqjFNDDYve7LtspV7TA6zSsLxtWAolLeS0aqdpekFFEKsGA2u9JmxK1A2pMeAN3hnNZYoVlzMur7A5pPxU0JsxGqQoljDN+HXh9FQYm2WYDGOIWG8RcdTNUCQRauGFgZtoeO+ES6q8Xha2cuSMZc2e6g0Jy30JfDHODINQ0kpaK60W7sLK3Xzgm2o5LRcuG9yNCwe5x7vKV7e33A0js58ZsdS8EKtlWQpvto3cEi14rI/QEiHC4B1DtITBEgaH94GlZoSrr1upT+slISaBAWcC3loCjeAapdOZ9AorNNNUEytFxyJ+QARS706SANVo0c+C1IY1upm21uPDoAU2BC2iHWBinfu4BHXO/ySh6iMBncNe5TsKn5YutzPWqvKhY/FM//2PJpirFbQVynZiW5401XWMiLX4pqD34GOfO/Z5rh+oxpFaJdjG7VRwNJpHn6HggIxtntvdM6rxepC3TC0rNSdaybhWEYRta2Qb8H7AOo8X0VtZL562bXqwmw3vwPtCLSfWLJQEZWusi3A5Z45PGw/nyv2SSEabFEtH93nD7e2BF69umCfHPFrubvbsp8qLuPHp4LgdDOPuDjvOrEVTaYedxVvHw8nwh9eZh0VljkvurAPnCNFig6FllU/VUnDo6I9SQXSDb5tgrJBb5bwJf3q98Pe/P/HnNxvnpVJkINXAJiNZhIenTPzTA199+ozbw56B3EesXQ9r9DMU6QGGrbBugR8fhPtz4f5x4c9/+Y7vf/ieUlIPQNWMZbyhmZU1PUFL7F4+5+YmYpwmpbQCp+OJ80VRi2tbeLw8cjyeWJeNLV8oVXBmpPVFFm3obrb/Hwuta3G9Lq687wmtnR/5cwKWprXqEsIH97GwXuezIQwMw4AxsPWETu8Dznpao4cISte+uY9/1jmD9QUfDTE4zZRqRS104oGrpVa088RRJSKiM0frPD7oqEAzqwx+EHzZcHGg5hmRSq2FrLMF1coGy8RMLo/gPI2BLZ2QM7gWSFuletX8GQZG53FOuEjmKJalOJ7WhLNOczSj4ZQXTmd4ERQA/e4h8e7dEzd3lslOXE6F8/ZEckf++W1ivhx4OQX2zyduXzxnMA5kZUuGtlk2M3I8Jb59PFM24TYYPtlZbgZwBJxTg0C9HKFVdQgVg7cDxkVS3lSeM1qsV4B4kEBwKjp3TrfO1uids3bNoXdCrgZaVsmiFQQVf+cKRYQ1Z5aUybkhHUFnzc8Xn3QXkcdYTRq2TufA1l01jebjv2+tBcnq/rEVI5pArDNe37/m9Vm47j8ymioqGCqDD9zeTqR3EZsr1hnCEPDG05ouUx0BwwhtR3OezMrW+tLOFdywx08jcc7EsIEXChMlR7BV+RBp681H1RkqWmwaeqW3pqoMzllqNuS64cWQi45pgjcgieX8yOXcyJuH4kkrXM6VdYNzEi61Upshes84eMbJM+9mXry84+Z2TzCF3RS4GQY+GRK/eQa3g6c2xzlH1ofGza4x3FncYCi18t37zDfvNlIprKuQRTBNJXvOq4KjtkQtiVqzHsjGEkK3DYmhiXaWp63xuz8n/ts/feC7d8IxjaomsVBFu1PsiEjgaam8PyZSgTHSF4D6OdprQoOoxpUKTxfL20fh9f0jr+8vxNnxxVevOF+eSOtGzY3SIdvH05l1eWQed3i38fq9J07CYTpQ18w//v0/8s//9AMu7pjvJrZ84XQ8cj6v3D+dKbUxzTvStnI+Lwx+5rDfMw/xlxXX66zr5/9b55mKk2siiiMzPylapfUgOWOoUnRjLULwEWsttVZyTqS06ot7/TPO4Yw6impVjarCdTzjMBCsZR73jNOIcQ3jAq2K/kPDekccFLuH89ikOUbgaCZ0B2pWknwzGnEsIz4MyghouugpVQfmUhayCM5n8vnCtiYeM0wSiWWjSsHZgYp2ZC05QjWMXmhBuzZnRnKpLBSGVjHWqmKhCq7NxHDH4Hdslwc+5I15PVEXuLEDJ9OouXA+P/AsR6IvhMHp4WRHTmnj9eWBN+fEN+vKpTmiMZR8plW9DosxVAKuU8FqrjoCq44YImILgt4MNIHT6fhGVAYlVhi82n9tNydIa5QMyUEuhtwaBUtonstWu91Tc4ZyyTycLrx/OPHqs8yBXkiNLsO8tz2Yr48EvMV5lVsZaz8WY66jAHQ5Zch9zn5dmqmBwl6f1Y/0tgKkrpQAh+amBSfYlsnbCk55o3HnVYbnIqagDM9c2C4b50vi3RP88JR4/faeu5vIv/vyhnlM2LiRSNjNIdV1PLH+/c4qe7j2w1o5GCjoXRIiC842xEIRSy0OI47gnH5mXWqk2A1PKYbSJYXNWDKG0nTGaoFh8BwOM4ebG6YpYpoQvWWMI/voeD4Lt1HIdeK3Pwj//O0HWv6Br14N/G9//YyvPt+TE/z5TeGSoFXhsuhIZzeN3IyGMQiLZM624qIFNyHJIFIYosNb1amLrZxS4/d/OvNff3viT28z56pxS41KK4KVhsmi72KIWFcpV9wrV1u93pqk6ezcGHDGsNbAu6Ph9dPK++WMHS3PbvbkHJgmx3K+kBelYZ1XAIvUwvly5Ou/PPH+8Vu++NXn/Obz37AfZ759857f/vOfOJ43Djcz0Tdq2rgslfOicjIXzpRaSVtisGeehns9BH9JcY0xdm3qT4W2lAI/k2Zd3TP647j+t2q+1jXPqncfIkJKiZwTrWlygRZSbfevnYfGvminU+sV2TfjiT2JwOLdSJVMqZmKUoVAMM7jh1E1bUVQF53vjg/XZ7Sd+tjoFHSvsqRgVTQiK7UMhFbwfuM83HM6PmqXBzQfkGDwMsDZA5mHS6aIw1ZhMI1n3iHB8RgDa6qwNV4OgX/3bORuygxDw0eYpoCUkfVpZGiGF/uG2408oFbWmwbeJO38amLwkVUy//L9A79798AJhxAYveU2CK+GyI0rBCsIQenz3mNbo5lAMYZLTkg+0yThXaFZz7LpZ2ZsVIAIuriKDqJzuADOqyc+Z0MthiyGtVSWAnUpGFF0jun2QWlwWhPvHh54Op158fIl0LtQp5vyK4jFOde1rzr3VoWB7YAPtVAqF2IFk6DV7kT76QX8aQyAzuZaAZP1yWwq9VsuZ9bziSaJUha889ogtIyztc/pVmp20CJWVND+4cPKcRupbeT4sPE0bjy/jewOnzJNDvIRQ6I1SymesnnF7YmQpaiMyeq4w1BIaWVZV4UJ4SgZTFb+hI5Fgo7FasAiOGMpKEjIO+W/ioVmLNao7Mk7FNo96ntrmjCEkdF7DqNwt6s0C3/4fuX//v2Fb+83xGS+OVe+PzX+zyfL6Ce+fV/ZSmK9JGpzjIPj+ez4zauBMVp+/+MZkYIYsE6XW94Lh91IcAZjGkuBP32/8Xe/P/Pdu8qa9aPxTnQBaYZ+m0hYo89YdI0Q1MQgXcqlz0tfp9SGEUGa52nzfPu08e3DI9le+bMNK4YxDrhq2WpRoDkGmsO7zDQYMIXT8cw//PZf+NOf3nC725GXRrJwyZn13XteHgYG29h7x+FuAhOxbiBXoWwrkzME7/q44hcU1xB8X04pYKGUqpbTXlh/nkRgjFG5KQ3NplLhv7UB6RbW60t0XTZYq/o3aKSccc4yxKkrDPQE1E63Ki3KRJwL6uKjf18NXWa0qi+1Gpb1xfROrzBiNaxUicR9VqTELe8CGKdfp1/ZmjU0bzEUghkY9zd8+PE96SmTJbBsKy4UPpt2zHHgDPzl+MQ745DmuAuGL+ZGNomTbJjSOIx7/ubZgf/wyrL3Cy40mr0wjMIwGNJ5Y3AWFxrJg3GeWwpzEZprSGx4r9DwLLDYiXMrnEWYw8jntzO38sgzkznEgPUz1u+IcdAoFSBfCnbn2YaV49OPpO2Cd4FiIuctUVbBx9BjeRS24Z1nGCPWaT5Vo8NGiiVX1WMsudCq4HFEGsE0hhgoKHzk4emJD49HPk8acW6uEI6PWWrdU44h14Jrmh6sXato1pYxeh20qcvwVJZ1NRWoTKs/uIYugi98dAEZQy2F9bKyLZt2dcHineBdpcmiGkxpeDvQjGFdMzVHjqfC0yUz7G746tkEa+PmsBHcxBQ+YyCzpCdy0ZGI82o8qfnClhZyS7q4iw5DQYrCQJwdP9q7oxNUv6nX3gSUYhAx3RgDITTEZ2rRLfVS9OfprSVGzzCGjyaM4CwxeKZhZA5wGFfmsfHwZPj968y7syM3fYdTi/zxx8Lj+QO3+ztwsG2JLcEwBnax8cwlPrOZcf+CP77tMdjF9Fun4dndyN0h4G1lK/DmwfBff/vE199XTgVNXfW9s3URayJ5WSlGsMEyOM8YjAaBmm5v7S4tY6PeZKQhzbKK48+Pha/fn1iNwceR6AfNpisF1wIi0HKlZqvBn1W4u5n49Zcv2M2BN+/f8Pb+geNl5fFpwbVAGwLz3YHYNl7eDez6nkYa1KxLU332I6Z0ZsL/RE31bxbXUpQ0df0VIIaI+1n6QCnlo53t6g3W5ZW+MM7p3K9HY+FcB4n0xZiIyqVEcl9ECTSrJ2+/ihqMUpOCXh0xlZyXvm11fT3jaM1QasHV0j3ErgvMVYhNnwGXmtUB4nSu6zCqNzdX294I1qtRIQR2zz5l2j+xPFgeZeApWeJ65JYzVhLZqmtpSyuDHflkDjzzWZdkrvHpLvLq8+f89VcTL3a1b+kXSj4yxMoXf5V4OCwcDhPJNurayKcNExthGvFTZNjvmaYdPtww+sbz50+M9+9I2eDqCgvsJ8eLmwP7MVAlMEx3xGEkLWfqlqhGsD3SxjRLHiZcvGGtYJwlDY1cIUtGzDV9AvReajBOrajeggqgKntXqVYQb/FiKNUQrEWsx4oh1cL90yO//8Mf2O12HG52zHNndeK73MrhrEekksqiTITQbzEf5VcF6F2rKR+fHxWzqG6Xa+NqrhUWrumiNOH4dOTD23vKVrHNqf5aktKXmtX8SrFYRtK28fRQOC+VHz8kzinx/HDkbmzML0Zud5UtvePpIXBpmVoqYZyQ2qhZgEQpChoqJSsu0Qvee8R0o8tgKK5QSsKarFKxphKfKgoyCj4g1eH9QHYLLa+kzbC1ylIqzZh+6NIlbZ6aM2YIxOCYouPZXnh1ZxnHkd9/J3xzL6wC0zTqkrJatlL4UBPGZ3a7Duy2hv1YuQ3CMznijxuLHVgumZIqUg2lbeyi5dnNxN3OgGSe1sDv/nzhjz8Ix+wxoRGbfj4uBGIYNTK8CWIEj8HbxjRO7KeR4BLWaL0xxqkzqlasOLbm+OP7lf/nzw+8XwQ/DJjmOB4vLMcFVw3ROLbzwmW5sKyZlAylZpwLDBGG0XF7e8NlK5wvT+Rcu9BI8JPRoug25t2ewzwguZHWhrWCdxmThVaqBj+GX5ihlVL62LleFQGud59XaZb+R0/XawW9alxLh1W7PmfTgXSjFp0BKj5SNWymw1lqFZzV4qpZQzp7ynVlKxcu2xPnpVDypihCY3Cmv5wIOV/wXnmQCnfoJyAOWqGWTUcNrcN3pWDoV8wm/6MI3epLMIwHbl9+xtsPha//8g4jlc/GyFYqrgjFNG6cw80T0QW+2EcOVkhr4dVsma1jMmdCA2MmhIGSV0pbMN5wczuR2x78DJyJ9RHnPUEUSOzHPeN0g+t0p8PQ+OoTx59/rKR3GVMMhMLoZxV4B88YInM0LKcTy/mCU82cjmeKynu8n4jjDpGVZ+7AElbWbVFjBzpTRaBUvYoGo/PRWorCqqNhRMMdKYVThcU0BhfIYqDP5deS+f6HH9nvvubLLz/n7vYGQ2dIdNncte0c4kiMI8r47ExPKq0lYMUYJUwZezWmdJ+5qf1XuiEg9BuW3oykVSiVtlVsMZpmYAvNJFpPMHZ2RFogJTg9FY7HxrqBtwOvbiKTF4Jx7OaZeX/D++/f83h5wrKwO8xgPLimu4aqHNbggj73Ta+1tSgnVG9uYGzFm4K1hSaC62qbKqhkrmmkSulg5zVbjtnzPmfORdOZQ/RMo7rb5LrYpeq81Vfu5sLzvWNLkW/fbzxtDkxVJ1uz5GYRG7CDJ4yBIhreOU2ReRB2sjDmRIuWD8fC8VyVIxyg1sIY4dO7HYdYqQW+f5f5l+9XHlPABIsP3eDtAkO0OApZKtbbj2xe0zJTjNztGt7Hn5+S1LKRS4E28P39yv/1D9/xd3854vcH7NlzuSQ+3D9ipHEYZnyDnBaWLbMWNQfEweBC4uH0lg9PwvG88vi4spxW3QFEFfBlqZykkJfGRU68TIm9mZn8zDgKziWq2RCjxTWEXyjF+rnt1TkVel/lWR+/gPd9Q38NGFQHTZOGcT06ufvMVfNX+9WwYZ3i5cAiWeEq3hpCcIBQpfRZm5DKwmV94u39jwQvzF4I9A/H0jWQArXQSs8ZqBu00ntXi9SNsp10bGE8MFIKlJrUryy67Gqm8zVxNOPxrnHzYoe7cbxfP2ATzG1kTLrtjMEwhcLghN1gmERwWGKwZOfAT8zjjiqeVJsWzOZhgaVZ/uXNyu/+WBgi/OpXlv1uJk4GXzLOT4zTHd5OrOsTwRXW5YRdn/jiZs/6+MgwWj6/GTlMnmgNA44hRGzZKD3mZBq6hbh6luVEyRNxvCNLJYTIGDymLTQyvkEq0Kql9U13a4baVJpvnOvFyBCtI6TK3M+irRXOF0MrhlIaBVVwOB84nRfevnvgV599xm76CeV2La7e6szV+eGjPE8fvwpsGJNVZnV1bnVlbfsfWla9xVyBz1ydW8ayG0YejeGyXnAldVee9NmtFmWpnvWysm6NKppUe4i2pwo0bg63zLuZ3XTgXk4s5wf2t+CjYd0WxBSM6zW/w7JH6/uzrckJuarcUGNLCsZUbSKMjm8kKRWtNoeIBeN1tiyWdfW8Pm98fzpTRD4iCV1PDzCmz12HwDxYbifh5V5jo98+BF5/OJNKxntN/TCoK85Z1KnVhOWyYMVwGAI7K4RVaDVT/B3vTo5LVrMDpuJs5dls+fzOMTnhYTX8y3dnvnm/cCkW7wym6fhmNxrmUNnWRCqVtVaMc+zGwBAbzw+O3dj6EGvueVmFJhvWFD6cHH//9Qf+8OaBD8eFsCUwlvN54XS6YGnIeGbwVvcxxuGHWVkToZA5cn8x6qATix9GDjeWsi3YujEYx2wMG4ZUGx8uG5fLxo2vvNoFvrg5ME8DS2tdk42Oqn5JcdUOrxdWr2LsK1fy6tCy10VXLfpgGxWEW++7d9xroay5i3vVlSOiHMuUkl7FRbtPH6IOsJvyBWrLfYmlWlbnNWxM3E8qBb0+qptIBySo46pcqLLo9+UcTTI5H6klYa2jtYmyObbtTK5nXZJcX1HnMXbo2sXEui2s6R0hFNYF7hfBenWQ3HnDzRCIsYJJ1KSO6TAGgvMUEQbj8DbgvSFai7QdrTokZZZ25s3xyE0wfHI7K/3JN4L1ODcQ/A7rhJSP5KWxXk6kbeHzm+fsf73DA3PwDMEqNMNEapuobeXkBr6/zwyPZz71kZqeuFwW4I7cDCIbozU4l7B2I3rNuaI5ilWKln4+6n6TLquz3aLqMOx2gTFUhtIwfoAI+SGxZFVr2P5ZbUvi/t0D62WlHnb9efmpwKr8ymP7uEA1ywVp6WNxtWiHaq6RQtci25eUfPSt97wl1OBCbWynM3VdQDZKWft9qx/8RfF3InqbckF1vSEIHiF5R7KecYwM0eOdY38YuTQobePDw4YzDu/175IGxnucj2ACBkMtKJcYwF41uZ1LbVBYizq4+00KnGjzsW4r66Vw3uDtZeG4ZjVIqN9co7SHwM1+IEbHPAYOg+HVAZ5PFiuB+yM8nFZKWYBKMLaPFNQAFHxkW1byZeUwTwzG0NZGXROMlmMbeXsRUr2OYwyztXz10vPqRn92373b+PqHJ05JrbmtNpoz3OwCf/VqxNF4fS8cFzU7TNYzT54pwqtnnnnK4AoY5fA2LG2zPB0rf/u71/y337/m/WVT5kLTm8rhMDHNHlqGPmY0Ap5OVfOVZhYkVMRHDJpwEcYAeWM7CvW8srOWnR/AGrKFNavL0RnP1jIXqYzWEoZIMDOYRpH0y4qrevK1sLq+cGj/SttaqyoCfg5wcTYoZcoYRLJu2bvcSrvf/HHkIEJfOGjXAo1cFoqoh7eZgjNea6Y0xnEmDoL3BW8dll7g0U7bomYE4wTnMoZMlaJ5RbWAKYg0ti1TayMtUPMjlkc9yc0ITZQVYDxCj/htjf/4+R234w1/+9+/43RunO3AYGFyjTA5DjvtbiQJrgvZSYItBVMKg9tBVdeZCwOrZEROIGfGfeX5beOwL6znM9UZwjx+pO6EaLDmlsDMs7s9U0ucSmPooYHVW441UbeNg7OM24VcMr/79j1/+4+veRUt/8erHXOsiInkKuR6ZIyFUhq+NWyreOOAgMMRXENqT/zsXWgTjzFN00VjwFlLjA1cw5dGCOhs3EZqC7y7qC51Dp7QhKd39zw9PvHyxTPtfuAn9Ulz2nVe7dF9HNDa1rfKVUXb7QqThp/l9nx8aj/+09NeW7OkJfHw9h3pdFQYuNWxSGsZQ0IquMGrq9DDOFiYITZLKYVSNGak1kStA0UM834gxgPnxbAsah2OXtUNtW3Y4BhmlRk1NB6nmYaNjmZE9aF+omRhK1kbFm+xQfF/rQWkCWm7UPJGzrA0x4Kj9gOldWWFd4H9PDENnsEbbgbP87nxcifsgiO1kYdLolkYBlUYDAGe7XVkIgwKnzmeMDUjS6ONk8ZVS2Exhodz42GtgNUxS248u4W/+mziZoqcUuFPP5748bHQ8Aze0WplcvDXL2f+5kXk/TnzrrvyvDPsokb47AbHJzeeYDVJtrRKC5lG4XQ+8oc/vefvvn7P+1Sp1n509BnXsE4IOvWiiqFUMEQs2qg5W8CDCxFjA63pQp2GjvKNwQcN99yPE2MIeG8pMuus10ecH5hGEEk9JBOMabj/RQwF5JEAACAASURBVAX9N3WuMfQNfdXlkzYF1zkWujVs1wgO32dlqh2Uph1rrfWj3VWkkHLu81rDFV5xBWLnvAGa7d4UekepReNSUHj1MJou+VEgiHVWGZ/uqiLYNJnR6lKgSSZL7u+xZUuZy7KREuStYjnh5IJrViNRPDSTEFYwghdLNIFx9Lhnwj/tDW+etOCOs6fERjFKGxqnGT87pC7UkpjjgE1ON8BbokjBdUxgev/IkhfOWyO5wGOtbBSMnDRw0GsCpdAY4kv85DAiWFcYXeXpwwe++/AWawfsbubHx3tMTvzmZmaIg1pZ05k5GG6mqSPVHMLIaauEfSOOFm8LUjcMDYenVDVcWHQT3UxDjPQ5df/spVKq6RZW3X6PthF9w5mCwXFOhpQb4gy7WJniRtne8fD+B5bPnrPbDUjsV97WveOdtnW9zl8LK+T+YFeMyXT5B9fQzCuZXv+wgGn996BslQ+v3/H05gP1nJBNsXYWvSFZpxIy2xNPozdIADs4Wq7kpsDzZU2ElJFjZb1smLJoiGDxSLbktSDuimushKlhXAFTKUYP3mb4yeRgPDGMlCwUEe2YfadnAcE5Yqlsl63Hv09camITjUxpQMoFYwzRR3bjjJVKdIGbGPjkUHmx9/g4c1wCj6ez2jWNgCm8uhv5T7++4XjK/Hhv1BbeCt4bak7UAqOziGuczMCbYyFXHQECjA6+ehn46sWeMQ68flz59m1iKwMW0wlzhk9nwxd+Rd695ekykNOENGVlDaHhTONutDybHbbC6bSx5crhbqbKyvffvecfv37HD+fMhasLtJPRoDs1wTiDIWCkb89RrbMxRfcyJtCag+5oa1lJV81rtEw2jtWh2ElrmX3UHDxn8KERTMWUpDJFKzhXtcv+JcXVd2QczbDVRMlZZ6UKNKWJ6cF/unjpOgG9tkt/+I2eqtAXYP3lVAPBFUfYRezQaUHaRSFV3RygqZQmAgPj6AmlQvG6RDDKvhQqQsVZ6XIfzzUSWF8CXTLl0np4XmTwkbKtLI8VW4Q2JgYLeIUxa5EZsU35oM4mXLCsW8GYjWeTvhBNCqlYZCt4X7G2YqPlcHPDzgxcTgvLemSgUUsm+IFlufDusvJQHGuAUC+kLfPpLjA4sL4QYoeLy4yfA40FqZlmDHYMLFTefXjLvtwhRSNPLuuFvRWcrfynL3f8+vY5kwTkcuR0LmQZ1NLptHubJke+fEBq6eYPZUMYdIGpKVqaVy+1IhSaUc6o7+muDfBWGABvC81ufF4MrULGM8yw3zl8qJzuv+fDjzfMc2SaI1dE5HVB1aT2FKFE03QwaEk/DyN9Ttm7U9uu69T+Mv3UuTajcc7lUrm8uSAPWSVk2VCTchSayYyTLo7O66oxMyXTSqFWQxbPKoFcA82BjTOXdWC7XNhPlWn0bNmwbl1x0By5JEoz2KCQauGCODAuYIzvhd1hRWWKCBpH42y3e2ZVrrSGs8I0NbYFLtvKh/OJc9oQc4U3V/zomceIM4ZgI7s4czdbnu8qYww0F1lT5fi0kZIAhV1s/PqTgS+eO75JlmAbpWT2k8dHOB83ckpstmC9Y0mRx1UQK+AEI42XB8NffzHxbPTkXPn+7coPHyBXizWWLIZDcPxqZ/HndzxeVhY3UNEldHDKVh2959Nbx2HW+WUTT6uqFT4eM9/9cOL148KpWpJpmKrhh1dRiEr19GBt15m8gMrwpJudvI5YrO2OQAPRYoc9BktJhVYKj8vCslbC4ZbondbO1nPmrIBUbFNNsY/Q2i90aIkoNKW1Rk5JGQFX44TRzlVP4L6UaFcYrnYOOpvzWOO7S0UF3R/BLT34q4r02NwO0zamb1S7vMt5cJrz3gggA7ls+E5WVz+2XsOv3UHf+2uhNQ7bdY45o7YG5xUhaCwlWe1gBUJYsVW1lYJi+IytNKPjj8k3/v2vZt6/TTzeJ6QIo5nUxUTQrKlyZJ7g+fM7htsRH0dwibKsRNdoVrCzZ3i2Z10SW0mMvvHpHPj8ds/LOZBzQqxTJoDN1NM91c/YqeF8wBhhFwcO+x3fvHlHXDyv9ne43QHrVpKDoTkml4hhw9XG2RiqeCoeP1qGMWLMDM5g48h6OZNTJhWDVP35WQy5ZIo0BGXDTvsJUHyhEFXClDI4Sxwc3sEzX3EuMjjPaQEbGrvZYrwhnd/z43d/Yr/X0McQPK0FmpSPTm3rrgCdjLGq6KBz4Y1xHaCsW/7rbepfaw6bMUhqXN4ubD+utHNT3XNWSE24Rsg4jxhHSSuOgiuwrIXT4rhssBVPwdEGx3lrPD1VDvPMuJto9cTx9IGcErshYrxldOqX97516ZW644zo9ZMGNQs1r0Ah1xNYg7czxqruuop+j1I0zsZYz1YWjstZ02ndiPeGafB89ulzDnPE0piGgbt55OVN5WbyGKNYvjXDkgSpK94VXu4nPr3dUYrwdBJyEoKD/RzYTpnaKpfLGUZDmA6cN081CUEj2QcLX77w/PplZHCNd+eF794XzjkozUrUsPNq57h1K5fjyqmNbAFqRxJ6q9CayRpe3ihEp1iDjREHbEl4eFx5OC0sJdPciOuAqGZcd1VWtU13aWWjaMhjr0fS2SJOB/h4p3Nm461GhVuH6R12sQ2Kjheb07GLdwHrIHpLtFXrnOi4SopH6i8srqCBaQZNDAgudumUaJytaOfQmt5krpxQlVmpOJzWLa2iV/0rBEa3wRrbINJ0GYVuWJvrqazN6KLKe4xzqqmtheD2VGy/HmaMNGhOFy9WkzMFFWzbqzynQd5Kz2HXDSxYSs1I2pAqOAOYTeet2N4hBZpZ+7XTEp3hq5eGN585fvtwouRITp4SHFvOFFNxg2oPdSZYcb4wDBVXC4M31OAwQZjv9rxaKqd84lWr/OrgeTZrlhNuIowT490tTCMpZ9p6IvqZIUyEUJm2jd0QGSd1n9i24q3HxQFxIyk1tvKGnI6wBJazo7SJ6iGMOveyPtCMsKRCbQ7nIxFDWrVzrWLIyVCqpYpoIOCsC5gqhlIcUjM5CQyGECyTHbiZd8QxYI3wYFegEj1U25CcOd2/4/0P33N7d8c4z4QYupGjk9VaxTS9Kdg+Amjobcn2rC1lUdT/j43t9WCG8+OZN3/4geMPj5iSdUZW0ZfMauihMVCy6I3JG5IULkm4rI7j6kjFMewDdqg8HDcul8j+ENhyYTkmcnVkKaylUKphiEOHzaA3NNEbkA9Go3iMJa+ZmlYledkFjEUk4lC3IKJQlFqy2pZxhDFSvEVsIzgFsrx4vtNgvSAMwbAbPS8Pllc3jXlQrGPFkFKhlEVvVLb9v5y9aY9l2Zmd9+zxTHeIITNrJtliU+puSd1tGILhhn9624LgL4b8QQIMWUS7OVSTNeUQGRH33nPOnv3hPTeKbRgCVIXKIsisYmVEnrvPu9e71rOYetGGv3tsPM4SWd71CkUlxCCNujQilph71lI3F4Q4L+4nwy8/99zvpFXh/Xnlmw+RteptyQi9adz2iRZPnALMw0AynhYqrQZsb9BKGMlLbHz7Lm6+dr+x2DMhRjnMvWVsBlWQemzkBVQR37O0UG+TL5VSZUCz1mxTp2E/DrjOYKzo8DllaiqUmDC14WpiHHs67eiVw26SpRxu0phgtbzAU0nULLjCn3S4KiWWqFbkwBLKldr0Uuni0dt1Ww7bLAsCBeC4UuNzEe3V2OvCS7TZ2tJWdcHL1NqMBi0Tk27ym92aglKl2bIWDtOOpT1Q0llYrE3RsqbprQ1SC11eKagly4a2VPlmVtG6VJVptKYLNV7wTtO5hnPy61RaNt1KQWOV5R4eg2fsFH/+ixGn4fJh4cOyknKP1Zp+gJudY/AGSqOEBTsZhp0l6w51DWasCw7L691A3wxzihi9UPOZaAea7WimUK2juZ7mDdE0UI5aFHHNLJczVkW6Tq5DKZ3QpWC5I2rPss6sKUnDQlDCES0R6wzHcSAr0aJVbsRQ0a0XOIcCWqJuE36pTcDZWuDFcV7Fo6gcJaVtMakE21YsIWmMHRjGjnu9otqZNQRqVTg9MFjHHCLPDw88PXxg3O9w3qOdxWzBAViAFdW2ht+rDKDEeiVErCvdC5GG5JUM1/+tNtKcWZ4uxGWllIhu0BlN31u5kZQCRaPpJMSQIuuiyMkzL5nnpZK1JqaZV8MBZ0Zc6TldFj6+f2K9PAsroMkk3Tl5tiTZo4hJ3ArWGJz3WOfkcJ1nclxxDpQSI38lyrX7OnHVgjYOZSRKG2hkI/5YA/Res586rJGDdewNt6Ph05vGzSitFEoBpcohXZJAY3TB2MKHx5lvPijOi2Y/Nqa+4/Fx5mkulKoZvCEqRwwQc5IXnlJ02vDVm5GvXg94rTinwjcPkQ+Xsi3qFLYVRp1xbeX5PLOUiaQHWnW0HHFG4fuOmBp/eH/i4VwwKmFJ7L3ni0+OfP5abJD70bMfINQek6rcvqqmlkipYinTTVOvrN+r5t4UnfGY3IhPZ2JU2H2PHrY3aqrUVHEVet8xdZZOIa0ITQuZrQksSLdCzomGorUssnhttPITO7SuMOsrzb7WthmirxAFEYalzrptkbWrD1HifFcPaq1CLXJWiP611O2fgVoK16y4AXk4UVi9dZirSmcSu1HROcn0RlupWzqsFl6AIMIpKBt1SA73WiK1yuY7J1Cm314EM2X9QKsLXa/pbEHbui1W7Dbh1pfyON2c9FfpC5+/qrw6Hnh+P/Ld1xfev1sxxnOjDHfKsd+NYupOmVwTbuzo+4l0WUjPJ0iV3IRIVVNAJY11jmakjrqpiC4GkwwtaqIRXVoXQyqVFlYupxnVGoPtOa+J6hV5mSF6sik85TNrWlFJ0ZJhSZXUIq5qVFUMaNSysFwStgwYBZfLEyUWUoKUC20j3V9jryiRcUwDoyIlJ/ERG0NFMS+VpTSCCuw8dEPjcGMwsyYWQ91y9LpklvMzD+/fsjse6McJ2wmKT+SjBctWN77FIWVsqZtEAy9Nwfwp1P1KrBd5KqdITlFuI7XRcn0JbZltGs5B9gFLWMgpE4Omlo6iE7llcjOkOTEtd4RiceMArRCeNaUOLOuFoR+hVeZlJcaVw82EslvrqzEoI91OVGmNVbphncZawBia1qS2StgMKzXUVVFxpAoxzzycL5yjtMUas8G0+57Be6ZhYN97PtnB60Oms16ut63QskxpuShybegcOZ805+fK4+wYR80wdMTYeL5kMh7tQfdG6lxKloXx1qR8M/T87M2Ou6mj1MbjWvnmobIW0FrkD1UkCVhrZC490d9QmiWHQl4C1ossd54Xnp+fNjSgJueAKTNffUz8L+bIl0fLzf7AzVpZFlngxdBYY+X5Esg1YDsYnKcb+803H2Wf4Cw2V9plxl4ypkZUgtwVXO+wWypv8D3HrsfpAjnL0vZPnjFtpI+xVUMuEgKRfsRM+W9UEfy3ZYG2PX3/P/nZeu24UlImp/R1ohVhWWDacvWu2y+ylEIxCqOEfBVj3DCFla6zfHp3y1ClBrjvevEUTh2mh2m0/OzTT/j01T1956l5oOWZXMKfoA4Ftk3dAgpcM5J1i9NVwlzQVlHLTGkPaC70vcYpibdJ/a+VBQsKVEFtTW9K91jt0C2gdcb7yvDJDt+NVPuO82Ng2t9x//oVt7dS0YzXmF46h5zbo+3ImiHNZ2pKpAYP50CaLW9e7fHeSQ9mnnHVE2bxEKeSSTXTj5Wu79BLJMdGbwbudwfy8p6aKyElLvMjH+uFh7pt1S8VNReW0FBDx9R7yPDadbgAdS28Ot5jdWNZIrEtW4trJSbBDirTNp1dif3HKDRS9SEPgmeNjbAIMnLyPW0tEjxwCj86TLHkYqiqgdGkmnh++MDT7S27ww39uBONsi4oNYOKchBuD7ssKF6eQNj21mpzJ1/1fzZJp7ZKzhEoGOvQVW8ykbhVjNUvOf1SCylCqR1ryDKBdZoxa1LV2OFIWBVPl5Xpds9uv8deVnIs5CzNJKEpUsoMI/jO0U+dHOotUqnEUjEpYatAcay1NAJ1k79MlYANNKyy26LQsayJ0DSnqIhJFjnGKfpe7FP7acduHLmbLJ8eK8fJ4Z2XckcUVWlK1oRYWZeVogPfJbgsAe0tNzc7lO54/+HM42nBOEfnHNZ38qIvkUqRl0NzstxNkRI6Llrz/ZPh3bMRxwMNpxWdtXRdZVnB6BuqHVnOT4KgzAXnLWssXGJirTIlqtYIuVGz5sO5cloU6sbjvWYYDF1pOLNCzYRl5fycCCmiVeOxRfwY8X0nB16p0uRcClMr7LVhj6JrDUpj1FtPXEp0teEQNkltV3nhCmIXgDko9AbHV63RNgh6yT+RLaCV/PQWWEJvB2hOPxpnW5MlgeJqfBY/7NWreJ12lWryAKdKrVpigLQNwQZffP6Gv/vVr2gfnyi1cvvmnvvPX+P3Pa5XOAeTH7m7OdB1AyXvCfOZqjctV19TOQiwF6kFL1W0UqVl9H/Oz6zzCeMXrLvgB4PDoilbSkdS82KPF6XVaC+V3SQqFs2IqmJQ1n3Pq881boTHdyd2zjNOnlLF4XB3f4/qM0uIQIftDLb3LOcibQp24HDseIoncobGgFYZVRs/PCWeT++J6omSIjFH+mnkzc2BOwvUiuo0R9ezOsfTmrmknqdL4d164qRgmCa8U1RdKBa0dWg3ooqm2R0xzNTiCXiZCk1HaVEQa9bh2Bp2SyG3DNrIDrZmlM5iSdnSQec5k4u8jNVy1cE04yAUp5wkOOIcKKOpUYz9p48fme+f2B1v8J2mlZWqAnXbSqPETiM2sC21pcSyJA0Y8t/Vnxy6mg1dWaRZV6KpFm2u0BeZREqBmAUp11RHU4YlRrCFvrccJmmi6I+3zMlSHs58/OE9lwdHvpyoaca2C5PX9JNFuz39aJgOA904UmoiJuGcFrm3Urb2WK2KNJ428aoaLSZ7WqNV+TpzFZ7qQ9R8TI7cIs42dmMvIHRr6Kzjpu/44givjoXOCsNY6YIqm0yi5Wlel1WqzzvLaVk4mAXrRkKA01zJVaxRaEssilSzfH/EYUxM8PEMf3xfOA6ClPzN+8rHs7haVJVBx/sOoxNr6kEbwvqRdX4kKQN+AGVY1pXcFMp06G0/Yk0HyjB4xWDlyh0SlOyF/7HJjrUYclSkqKmpsYSF+PYJTaPTBq80u77jcNhxNw3sXOZmsmgLGE3X1MbuBa8rmsi1n85sPJNaZK+kmvCPG1LBI1KlwIpo7qcdrqUUnHUb+FqsGinHDQB8rWi52mDkP6/V2jlviypksSOMzq3dMVcZr5XgxIyByWruvSUgCYu7UfPqzuH33UuKxLDFzZTFuBHjekoOSD+SESRZ2+QAZOCRqcCCKgyT4/Z+JCwJ5yzKebmGNYsm04ohl7rpe/JB1Si0kpbRXAK1iR2sGQsbItEZyzT2cGxMZiDGlRISthu4wbHrD6ACYUm0GnFqRbf5JbHjncb7SDNQlMMaxSXCr59PfJ8WYpYXUCUznN4xn/ekcWLqLT6LLGPNyA+nj/www3lNrHmlec+oO7px4HF55Pk009bCOShudcfgM74GmlE0f+HQGdZQOV8CnZOHKW98hkIlFoFtms5hvMUasaC5rc3VGI/WlRAyqjhKqixrEOiOljtMRerEdbHEoFiWmfPjR54fHzjc3dL1Gm1XjJGqnawbWiesKhh17Q3VwqFFnq8XMDZbyolKWxPf/+bEH349sz5p+gqqFWkWrUUqgKqiIMvTOUCqilQEamIbaJUZj46I4RxmuRIfG0/PJ+YPKzZXvIOpi0wd9KPCDJpuHLCdphnJ8KtqNk+2fdkBtBqppqCMgGJUsdQkEpbW29fQMiFW/vDumf/8u5nvTqImT73jMA2Mvpd2ACq3XeDNUaQCtsFCb4eFso1CYl0vpCUzHm+5ef2GIT5x3F9wtufpqbFESR26rkPpjiUUlriSWhHcJJByZc4z333QzKHw7UPjOUBGJkhp6xUA0+WSKAvk9oQ1hf1x4pwa+AHfdSynRxSyiVfyHgTlMdpyu6scp0aicakDoXliXsWnaxSFQoiJuBZSLIQk1D6PyC27XvOq89x5x3GwTIPi9n4EpMwRBakUVMt45+jtlkJsTWq7tqSnahKeLyGTwkptGaMTtRSMlqLNn3S41rZSKKjmqLmQUhIBedv4i5m8ce3PunYcXafVUhKtKYnDbrYsqXKRDb5WsrHVgCkVu026dQmU0wXXGoOV+GRteYPBJKTZWT4U2tot/mpkgqUh/eiI3QonzQI6on1lf6OZ9sN2+FdqkUO5VTHLK5tfuunFgKyhiW3MbDyX9lInUlAk9KbV6Nrh3JGSnlnigsqKu9WwY8IaxSnNUKTcUDVFXFdKTmjdc9iNNFtBn6na8C7B1yFzahv0xoBqFVsi8bLyYclcOsc4dgy7gYwnVss5Zk4pylUmWubzgsmKFArLHARpFxreWB6952bcJouUWbVo16U01pJkOnQQUwQjve9NyYszVovZro7WyFZ26ixdr1lXAIf14tWtZSWmhZZkWQMaVQ0lRkJMXC4nTk8fOT8/0fUW4xewK8o0CoHeNyZj0K3C1k4gZEm1LRw3OwhaAOE18f037/kP/+s/8va3M69HzecHT68FNampaOXk8VcQUhMXQMzy0uhHqCvn+IR/fcfheEv8fqaUj9zvO8apoo6WFhtGCztXuyTIv9GJS8J3KGuhFgxe6PxZoev2jLe8yWoOmrRxhJpYFpGbOq8IMfN8afz+3YU/fgwk5Tn0huNuZOwGpmFHbx2Tq9zvFnaDE9+xMmxCORjpCKtszzia2BqxZfZHz5u7nlonnueFQmG/G7FuEO9uLKTSRDPWiNaoG+SVDw+R797PPOcdyjV6nySu68AZi2mNuF6tUoFpGnBTz3rZpLe8MNiAqpoQNblVCoZaLfvO8PNPO25vGpdQeA6WOcISMxWz2fTkfAkpEGMmpYRulbHzvN5PfLq3vN7vGXRj9KCtsEac7bDOijddSSWPc2C16Lm6bFLiNoErGtRKiguX8xNaQ985ieTXjG7hpx2u1xO+XCOQpWz5/61BoMq1rG72pusBq3XbUIVlO2ilv0nrH2UEp+RwrKVgjabkSoxNMGaxkteMWhumFJqR7qYrnrC06/+3pDSqOM5FE1RbfuSqvTWB96KzHPa2UKrYrcDJS6Jl2kZoorYtICMao/RRKKDflilShKeVkgy8blhn6Hto0ZByouTImisGS2kebbx0JBkr/Ul6x9ItnEogXFb5LVReQgKtsVT4EBILDjbQd6mVvirG1qOzIW5JtKYc3b7jdm/56lXlcX3PHDIZS46NQKBvnsH1DD4xrxsUhMqSEkNy9BpyWCh2pLMO7zup2baGfuqIcSXkQMpJPIZNiX5rDPuhR3tFJWDUQmcszncCiS6FFOQGpFvDKGk0iDHTWDdNNLAuFy6nM/PpwnQYoV54+/gt5/BMaYGvPnvDzz95g9Vm02C3sMAL2EVsN4+PM9+/k+/Lr/+vt/zn//KEni2DUlz6QrNAaXgPugNtNTFaLkvm+aKJwG7s2B0MNMW7+QzNcX/7in91Z5nnb8EbWsyoNVFipqaVuHbE3HC9p+tHjB+oxopzhbYt+wqojDENrkvcItd3dEVr0dJpFaUkYllz5rxmnqOlqMK+t9xMjqnr2PUTnfOMtvDpreHNjaLv3Mb62CZIsyFAixDNnOtQzsghvp45TooUBp6eF9acGKYO3w+sEeZVQkPohtVqI8yJNtpU5RQgaUuxDafFvy6Rd+hsQ1dpLIg1MHlEf7YWoxvOam4OMI5H/umHhW/fR9YkldhWw+f3nl991TFNifez4RQMcyrEXOVHFGxoaz82omgtqMFj1/HlceDN3nE39nir8JMlacO6JqqDru+2s6swTj2d8y/ykbLyPRNozwbuThHdKSielium6Re3APUnugWuAOPrVl/wZg5jzEsT7IuuudW5XKHY14WX3a6M1rhNrBf/XqvXZYQhl8JpDqyx4XHUqlhPC2+/fkuplenTierkulG3znOJXWpy3roOjBywDf0iTchf6nVvTCPSaGitQEtnumqSCGtV0wy0JNOvUWIyLi1TdeLaDKuUxmJQzdMY5ADHMU4GYwqX57N0vBdZRjSlBOGGxXc9Xgi7aPUMBdJShIRvG7ZXpFR5vxY+zoHWLKZmfLb4ZtjRcTSevTc4Wxl2npu714y9Q9ULv/r0FR+XwON8JkdFzRXtNVYZasn0rqNUgdskpSjGk5UlJKFNaa04x0hKCWc8ve/p+0GA1Ks0wSqFEMW0peKJzVFSobMW1TKtBHGKyHuKnLW0v27b29IitYjNCy36Y46FuGSWy4W4Hmlk3n54z++/+y3oitWGz+9eMw6OUstLv5acszKh5Vz5+uv3/P3ff83z08rjY+B8gbElLklziZqWEq4qnHIUB43Mw0nx3YO0l467EW0i2ioezvCfvl5Qb7/Dmnv+6ss7bu9f4aeellbS8kxYF9KisQG6ukPbTlgBVfy3gj4oG2aziCSARZlRDj9dMaZSCeTqsNoxjntKXqWDKxYuS2FJjbH3HAZJYg3OMfYdu07xet/48pXiMJnNWy0Ef7WlKNEaoyzeWMZhYJpuwRU6X7Da8vyU+XjOuF7kgFga81rEelVXnFboqkklU9B0ViAwzXqacjhbsQpSRirB9Yhuwg8pGprzKKew3nMJmVY0fV/52SuDHxzfvl+EWaENLTcOfeVf/3zki3vFmhPn4AnFEEsmxMIaEjGJbt73nhiyJBZrptdwPw3c9oZeNXRd6PzE7rCnmpEcF2HeOnnZKSre+23a1wz9FTJVSDGJ5toK2oi1zuIoi6QXa1O82E5+yuFqjRNlq9WXjdkV1pJSElP/Jk+29uOPWiV04G0nhm9tZaNbyxY4uNq4LEpvFp5QWNeMa4aWFevDyreX71nOmV/Yn9G/6lC2oWrepIYJgaxczsbI3wAAIABJREFUUEqgu1o7VNMbgzZhld2kYLWxLuVBR9dt0pXcN1Wa7dsGXZau+60u3GgaEoUFZCow/oVs1LQjqQ50Dz10psMdJrp1IVwyoVyIaaQ1hTZCxaJUnOsxxpK2rqJhMjijeV4SP1zgcbaEmPGlwtK4nyZGN+Bro3Ma3yesrZhWsFXjtMd1np998oo/PDwRcqRZYYjW2sgxYWiMTrbHxEbM8HReN8ybJYUZnTO2KcAxmgNoTy0rrRRhwtaCtha0RDkfny589/33fPbqhk9uR7xRlFaoKrA1m+AsqKplUq3CV0A7tHUY60lRMZ8XTo8n9jeZfXfgX/z8X+F3nt/9/neUJJZAqoRMXmR+DddqF2M1v/jVZ/zPQfHv//4fOM1V6p/TI48f9xz0LcYpse0FSGsllMCHuTLHQNdl7g+O3VGT0fzxw8oPl4k7p1nPz8SLxfa3+ObkFuUnwKJ1xnU9FoUxveTXw4WWApJGRPjA1rOmxrpE0hoZJsM0aYxJGKVkqVWhFU0phrQ2QsicZun/mjrN4BRj5znsJo67jtsp8sW94W6ncW6rW1IbO2CryFEaqjI049HbbqCawG6o9K7n8QTdYJgmT8yZJazkolEq42xhdOL5rE2RW5Mp0XqyapQS8e1H61iplZI1UQsAPGXZrE/jDuctZY5gHOOkOOzg4Rx5/3RhDsJwvds5/vrPbvnLrzq8vvD20ni8wCVm5nUlbpColAIxLJS8nScIg3kyhp1TWDJj1zOOEuW9WvmMN/RDB5vn3WpDzZCauJXknCuyKL6GElrGWSgpQolYY+QsE5DJnzCt/zsP13p9mLn+YDtQ2wtAWywK5iVgIDUrSqwgaEqplJy3f77QNtPvtWmgbhptLY2QClPT0uJRFHktPLUT5zcX/N5gbZPlQFrEp2a8bD5rhGxQrts0UiRcoBQGA8pv1TPSYllb23Ra0W3g2v2Vt6lrS5lZjdEdRleZ+JB0kGi6DWUKGE3TiqyiRPKUp6aE6iquFmIOhFU8vNcrW6niuqgobDdRoqMqCDHyFKC0Hc5V+nzCl0anGqMSve7SIvf9jt1NR1wXHt6/p+5vub05onXjOFluDzuelmdZHxVpIB06xzAalLWc5kBRDZ0UISVcc5AVOlUm01NLIdRKSpFSrbgockNVhXGSpNLb5KiUYVkbHx9XboadJL6IJCrNRKxVGDpyVISU5YNgepTtaMZhF0OIihQD82XhdHpmuHHc3bzh5nDky/tfMA0e73YCtkaaI9ieoetjXFEM48Avf/Ul//XXH/nNbxda8Niyk8rsauWK1wohFlJWXFIjtcLNYWDoGl4bKdc08NXrQn8Y2B9HPtlDzTMlT9RcqVianbBmArPSsuwWUF6M/1okKgHSyFtAG0nCnednLs9PfOIPOLcTLRO58eVStpixoqmeQuG0XMil0nvD1Hccpp5hUAw+8tmt5otXHUNvtiFmSwXSEAPCNjRcb5C2vXTTeaspQVGVYugdOSvmVZOygEsUkU4X9n5HyZpqFDnLrSOQKRuQW2Nhk/us6gADqpFKZo0Rq6QSPdXAJciOYhgta3b84fuVeZUhzmvDF693/M2f3/BmF4ipcl4t59A4ravUzc+RkBLrshDWyDoHQsjkXJgMHJ1jpxqHoWc3dWhTKARidrQtSi59ZoWYEjWB1g3v1ctAWEqipkzdzi2tpWizppVWM0ZJJbxCb7fvnygLlLIZgpVAsa/6qvyhuPYfuU2DvZ7iGkMtbK2qG7xYKSBt5XVC9anXrHiTSXgOgaP2JESw1hihwl8C+yoMz5wj67LgB4/yHdl0m3XCUFPZll1NfIpYrPaUXKE6sb5s4Qalm8AelJZpplUJ+ai2AaKvY7/ipYV0I3NRjKSYjKVWT2sWZRISz4TGwho/khYI3StKBtNpSi5cljOXh7dc5hO5FlLLmL4jqUgtAhVZlhlrHXttoaz0StNZy7Tr6bzj7n7iZt/zw/crHz6+JS4rjcZOaXJe8cNAPyVCC9QmFrjOGG53HX3fcdN3hAuUKPUYxjlyilg2fmsRItXHj++ZF4NREVUjoLfm0UKtF1qSuOznX77G10hYLzRlMB5wDe8tBk0OmpgkLqvtwNBPoBVxjVvLaSKHyDqvXM4n1suedrxhf3vL8fDJFn9dyQqsATZHiQhfIhX9w2++4ft3zyyL5fd//JaUHxg7x7023E2Oode4zsgwkuT20w/iuXadRByXYChzx/5ux1/95ZdUdaGUM6SG2+9wvUcZ8J3HKAtVEZWhtmUrStTUFklFnDJta8BQxmL9wMF2dM7TXh/pvHyYK/LBlskPQkhcToWnpedduOFDfEbpxG7ouDnsuLub6HvD/QG+uOs4DH5LSl5RjVurh9KbeUpSjqou4vVuGu8MTsEaMsYZSoqsUVNah84rBxe4mzw7tyNWz3OWF4bVjVTEKTQ4h8lif1yz1Kdr40BL6CdnSU6Ow0jfa5YYWNZG7wIlZv74w8rXb8+EVCHBcdfxP/xs4s/eGIzKnFZ4uDQuuTKHwGVeCFGSljFEYsiEkCml0hvFq97zZuiYjIbcqDkzTB4/eVx3oCpLLol1WYlZ0JlWW4yNaJWFJ43e9Gqksn2b/BUK4wy5NgkLKulsazkhzMKfcLgK2UraOlv5EZC9Hb0opbBWfF45J0qp2/Sa+ZEhoLfmAtE6tdqKxpQcrhTRX6OCx3XmZudZrKGpgm6KWhL+MtM+em70QEgVOyT6o6bqnqp7nNJopOurba2R8u/0sl3eQgbyXhCTdtsWDFf+QS3p5bopL4KtiWCriBUbmRXZIYmgbqpMB81E+YaXAvMjNX3H+fHMd380tE/vuL/NUkiYE/Fy5vL0SA4z49SxzOJM8H1PpdCbStcyNopXs6XG3TBxu/fsjpbbmz23h4HOab788hVffbZnOS3QZnJuzDVhRs3uuAc7EM4BpzVT5xgN+Jrpvef9aSGmws3NAWMgx4YH2hwwqmCMYQ4zMVf6TuG3Q02VjBQRFDQJQ+VopQa8pJVkGuDQzZLWBsbSqjT1NjStaVIC14O1TRpXUyPHQlguLJdn4vyGtBjyZBmsx9oqL62WNmhQQjQCcbRe5sJ//D+/4X/7D/+J3DxhLdiUuOsrkkIurNFQ2kSIih7Nvkf6lAzSFNwcsSlCThys4f6TT9kdxeqXUxTXzIbUlOBBkeu++A5h83LnnIlJNDurzdbUKnsIZZDYbXUoMlVLXRK6UVMlrJmwZh7OkV9/88C7pfEcE1M/sN/tmHYDvtPshsZntz33+wFnzUuBI2iMdhs/torliyr+UV03S1xm8AIkaV407/USKcWgWmRvMl/tFF84mIi8rytz0cRcKDWhlaL3ng6Hd5qiEusa0dpRaFRVUEZtTmPFOHYUFfnm+wfevW28uen4+GT5sCTeX8SJdL/r+Xd/ecdf/fLA0BVyqMwxc1kNlzVJdLpJaWKOkct54TJHSgNvNK8Gz5djx+vO0butzBKw3jCOI8Z2EsYImnOIxKhFa7UZqxsGg6maljdWcJNkqbUOYzy1SVigbXwUccdA0YYtc/vff7gqDeZab8yPQOztZzFGriIppc0xsNm4a305hHOWsVmbbbnW+PHvK3H7YmCl8d3jB0iF56eHraFAk0umPf6O7h8tt/d7Xt/e8Lf/9i8YDoeNrGWkRqYVKNI62zYAbtFJgpCqiNsBmS6kunkzamOFzqU2QvxG07k2k2KE/pU3aUU3RQxiDLclgZ1RXm39SM/kdOLDh5l/+O3M99+PlJZ5/Vnic69xLZPDglOVpitmMLS7kedTI9cKNaNSxmYYtGAOfe+YOou3gc4poau3LSlmB5z3HMeREE988/zA45xopsPtPF3T6JoZiua4G9kbjamwJjGK994wjg7rNJfzhTifcbrhnJjpawJjtUxsrQo4uxY63eGco7ZMidIU4Y3FeIMxFe0swkDIxKRJqYjNRUt1d4wrTQsqrrMGaiKFlZQsYTmxnE/M80wfB7wzWOMw9mrxmylta3vdHAPew2dvdtAcD48NVSw7DMX0JKRoL2bFeSmcL4H7qTI4ASx722HctghtAT0U3DBT1AXMhO9u8QpxS2yJwhITKSZSa+Q0U/KKRtPMFkDRUo7pux6lFCkFWsgYpaAlWksSdbjuB3ITrTUrQm58mCPffHziqSh83zNMI9OuZ7cb2PnCp3v47AhjL8WdesN2amWxphfCHGC0E2dCA+0M6IYxiWkYUDjm5cwcErVUrEocHHy6c9yPFd2eWErlORrWvJHTDKhqsUkxWkPnLU/pIqEI3W+1NjLAFJTUB/Wa83Lhux+eeHxQdCqTauOpKKgTd53l3/7iwN/+ywPHqVLzypoSjzOcFgjLimpFfN6lEtYkS+wqRqnBND7tDZ+NloO/wnJEjjyfZ7STCDZ0wsdoms6N7CaP1QutZFoRH7DQxyq+czKcNUnuyaDmRNopWdxFpaAxWwr1JxyuV+3oKgGIsfkaFtBbOwEvE+1Vc71OraUUUk4y1VX+2d8jf8hiSzruG398+wM/8IHUqvhZaxPKa4ByLtjv3nMcOoxx/MWvfs4nr0dq31NyRE5U6cjQWt6aOQt/VumyLaoUKCtdO9Vt7ATBESol4z9Y2hZ/ZfuahTtrJLFWFMqL57VpLZpLgZTOLMt73r8N/JdfP/H77xe00uwePvLduwcOuyNenVnXEyqvWL1N7bmhq6cUhWqWkhohNtYSuek8e+cZR/CuMHUSS6x5JcWIbpX2J1XFcdVcPmT0sMdljy8Xxk7TB0PXFA5IqTCv4qBwOtLKgut33BxvObVCW4MsvLSmn3aMo2c/dXIAris5BMISqVmjrThFtNE477CmkUqgVIVzcqNpiMaV0oI2jWHscN6S0opS1/bXTG6JGC+k2BOWZ9ZwIsc9pe+pxaBNJ9NCEVHJvHAFpCTuX//1n/NX//U7Hv6P35JLIxfD83NlGC1+6HHGkora3p0S065JPCSlZJS2NC6YsWCtR+mZ1CIxO5TW5KqIBbwxGFfRJZPjSs6zHJbVbY6VJhOPbmJlLFnqSJqWnVyRZzVXJaDmJosHoz3GQMiJpViak693fzxwe7NnPzqmXnF/8Hz1yvLqaHEuC89W+e3zKH7Z62eyUTfbFyjtscrSOfDWE4NijYrcPEYHvK3se02vCvNaOCXNvCo+FstJFRIRWqVXPfua+Zm/4N2IUp7nBKnK4rc2wXrm2picpRsMy8UwHT7De83l8oGPIWC6Ha93Pf/m50f+3V/c8eamYUikknheKg9ny9NZfL9ULXJASqwhE4OkG00rvBo6PhssB5dxtqKtw3oNuhBjYF2i3BpbpBSLNtAPPWMvy6wYpU4KLXZBZyxDP9CAGBPrPMtnVBs0gio0TYsNsipp8v0ph6s0r/7zjdhLOZySKmtpgxXf3kvFtro2vV75A5vZ+0+qYZRi0zMMqonda22FlSabSF3AVLS8H0BZatFclsrbHz7w+OGBT1+NTNOey2UVL5pqqE1/Kk2qoVPKQsqnipG7sV3zHaZsi69SUE0OXiEtORq9TL+tgpLmSpBWWTsO1LgKrrAmalhY4iNv31/4h99U/vHbwtMK+74xx4UPj2/58JAY7Uw6LzAvGGEWU5KkjJyd0FVRVCCbRo0LtjWsTnKtUx01VprLuMHiXOL09BGyXNOc1xz6iaNJPF0yBzxuLeiaGRUMROoKz0+RxzkKRHnb/tKcUNWyxZgqNpum6IdRFkCpMvYDVKlL0U1eMqUllDGb97W9fK+s7eQDnAKpLHLwbAuwnLPINkVLIqbI1bVUOahLyoQ0E8OZFBdqGWlN7HnSqdTL5nd7PxcVWXLl4SmzzhqTRc5xxtFZQ9dZur5jPzk4Z3KWonVVlNgDayYtM856pqNld5wY+xGtrVS7WKE8UQutyNTVahSZQwW0KdQqDFJlDGT5bJRmaS1CjbLE1Wqzp4mEIMWYDYlZe2jCL12CBjsy7hKmVA5jz3HnmHrNzaT54tXI6xuLdxKkkdujkTJQe3W5bFVMKEqqvH3/zNu3mlYNRlWeHp9ZV01WBlTFanBSy8B5rRQ1EuNWPW0LK2KU1wjJztvAzsJeNYJ2fAQeiqAQqdKZlUulqsBlSVxWTTcccKby/PRAqppP9gN/+cWB//FXt3x627Bqld/PBqdgeZ4NS4jUKiWNy1yYL4llTYIOLRlv4X7seDV6piFgnEZZg7GgrULbrTFAiVyBKgxjR9+JxKQQj7qum2SpDc52KDYcp7fUKBY0JSMZxlRUFd5IVg6tf2L8Vam62avkOn3VUK+/ocDm4asvB2fbWK5CpxJi/PXAvToM5OeF/1pfAAmKqkD8KMJWbZvNRtJcBoqhlMrb9w98+8M7vvr5J+wmhzHjJi5v7gbJr8ly6iqktq2Sc7OC1bbFKGvcHA6OpixNGZT20ESvpTZK05QicoJRDqVXUDM5B3IMrJdn3n545uvvVr593zG3jmY1uUFqiafzie/fwmQrzAXWyugUg7NYlwll5XxKdOZAoqeqhFEFh6b3bBXInpor1IS3e2oOXE5Feq90xnnP7b7jl5/f8fZjIsZC84aWNIMzDMbw/JgpMdNCJMeCcR7joaaC0v0WXwVlO+KaUUXhjCfmgg7Q2wGlV9b5Qs4NYyvaKppR2wVHyYS5VYAbpVmTpiSNamJhCat4Po0SgIvINZpcKikpwloIYSWGhRhmStnLgYqRMLLqoeVtu9tY48rbdwv/9LsTH76dOdi9TCfKcOs0x1HRDQZrKr1aoQNXxWCiO8+wc6QmVq5xNzBOR443n9Pv7inNkuJKCkVqi9JKqtciw2uVtxLbk3GbPCabf8nnJ6qRIj7Q25/XVGPhWvWijSGmxvPS+HgpvHs400ridho5DJrBZQ6j482N5/WxY+r0Swmo3qj6dkstVcSG1VrdAjGN51Pm668vnM4rpQTWoKiql89aiRjdsMaTGiy1ob3YCdPyRIhBXr7ZiB8VSyaxlIhxFypHKFLpYqwX3zjglKPzissaeDw1nk8PxHlmnQOvbnr++s/u+bt/84aff2rwXpgTtcCaG6fZcFoKKRdKhfMl8u7dE8+nhVa34tKacWgGJdF4P3YM40Rtjbx9TdPYM049TUnqUOlGP2icYWvYLWhVt7CFxrsOZ8U7q1S5TphoYylZ6ApKgTGKXA0lWfL6E90Cii2FVRvWapxzLz1ZbZtor1XboqP+eN2vtXBltP5pRTfwcsCq9s9lhBdZQbz/2zQpCyi2ZViphbcPj/z6//ktv/rVzxmHT/H+SElJJil9hc1t1zMjvVui0chRW7dfn0iwWtJZOFrTNCzogYpc2wrywdBYWpGNbk6BEhfyurI8X3h4f+HbtyvPiyVri7Iapw1aF2KOnC6RH5jxtaKWiKfw+qjpXKWxYPqRHZ7z40w5KYag6bRm6ipd78jN0LLBbj1hlcjj04X3b1eO00RnDLo1igmolLjtGv3eSMtCspAhzIneWt7c3TB0gZQqayqkEKn6mVJPxJTo+h7jBlqYqU3j/EjJkUuILEuk60byHIgxYJtQl3wG04u8gpJSypyvdhYt4GJlBX6TMiU3hsHjvAVTGKdCuiRyUqQkjcBxXUkhULKk8TBCjDe6217AYuT++PGR//3f/998+0+ZFhx30xHTKjZHDi7R6YRpDascnUl4r1DN0EplzitWGbqxYTtwO4seRvz+Dm1HltOJuF7QmzFfV5l8U1MIEF6RsxaMZQrUVrFmS/XkjS3chIOrjaapvO1TxQZIlbqh1hSnUPn9D09882EmtMZx7znuHONYmbrE3d7xyY1j10ndd20Vo6R7Thu9LXGkHkeGZDnAjQJtOk7LD6ybNNOPXopGc4WiGLse33csIXFKC3V9wpYTSclLW5eBQiaVlVYcIR/4sM5UB2VrH9Eqo2ujlUbG0BvLfq/4cJlZAlzmhbKu3O1H/vZfvubv/voVf/aJpXdFtOqqyTWyLJXTDGuKpBxIKXE5X3h8fGZeE9ZI6aFVihvnOFrDMBi08yjnxZSXM84bnJdQR6qg1Fb/RKVm2RPIwXqN5Yv86ax0BorboeJdR84Zo8UGWksk0UjKMmdNXn+iz7WJJ0mmT/RWyyLXuVyEkn5NXQmJFaBtuf2NCt74Z9PqVW+9emRfptYtWPCjjHCdWCWZRRMwN1qxVvjdH9/x/fdPfPbJV0xjR0xn8voknAJtUNpSkWpqreXBqzlsMV5FbVbCAxVaM9A6SrHUZlHVywGMCNpaJaiRnBU5Zkr4yHr5yNOHC8sps0YniSwNfeeZaiPliNNicD7Pibw0VEioOLN3kc5qdqNUaKPl+hnSwjpnujZh+8awU/R7zcPTwvPTCe17puM9ymh839GPo1h3VEXrHuUt4bKQ08L9OOJcIupE0Y68Koahw3QGZw2XJRGeZpZ1xSrxuy5Lwa0w7SxqAxLnDNb2gCKGGasd1TpiKRSx+ZJDldSP0nROQROTdokCFS51q/JRmlrl57UWsr3WGmuiXLWTJoTAOs8sy0LY9DXXZemJ11K3LK3bilIMYR749vdnfvMPD7RqGTvNZA0717jtG1oFUghkPYp3eOowRrCC53XGrpFdJ/AgPVmG+z3NCz4vJYl9K4rYq5rISiVL0KRWRc6WFBNKJfwGs0kx0mraKojsViUDKS3CsShCj2sUUsqcl5VvHzLfP6+sNKa947CzHHaKcWrc7DVvbi2H0dBZjdJ587xsDoat3kiWWvJ55RqR1w3lMtNB8XrYoV1PKY6QFDVmWkwUIOTKOVZClhaImAvadPg6oquFLtN8YfI9vg1cssYuC0lLxZJuK2SD7i2tVHrfSK0yr3KODH7iuJv4m39xx//0N/f88kvPqMs27MitstbKvBTOcyVmYbWWmkWCaWWTniSOez+OfLGfOA6Orrd0fqSUhtGNcRhkya0sMTdyrsLGcJaSCykGdCsYnQVTClLEWSvrusqSqvGy0HZ6C1KkTMWSdWGeIz+cM505/NTD9cfDsNYqqaxSZOv//5lM6xWi3aoAgrlCp9XLYfrjFKu5hhL+VKP90ylWarbZDlypcBFYs2hJPzxW/vF3H/jzP7twPN4yHY6kMpNTxPke6wZac1gn29O0XoTGlSM5yXWpVkMtUvmgMJRsKFWoRMZoFJmWV9a6UstKyYV1reSPZ55+eOLj+zOlGdz/29559ciSZdf5Oz4iMrPMNX3vdA/ZM0MrCjKQQPD360EPehQIEKQEsmc0pvv2NeUyzPF62FF1myOQgJqYtzpAoU1mGWRG7jhn77W+dToxDCNuTYzG0JViS5ILpo0mlkJTipYr2/0CB8MSFVsEj6EgTXtjINWNcRw5HAaGYDmcAtln7t594jZXrtpLStbExdDLfgzTiNxHDQR7ZDSWwSqUKVRjUHrCDobbZebhYcbZEWs9zmlpPYSR2irWiZ10Xh4Y3AGnLTHvWWjGoZ0ntkpVFmVHMSiUffi4FJm4ozC6Y7oclzG70WXvwdcqbqttLQJP90KMssaylUyJkONGioswDVJmeDohQe2SgCCncc1gLnEtYBGhe94WTLCcxs5xVIyHE7TCZDXBKqxpoArKCqe194pxA+PlkeHqinC6QNtAiXLKqa1jR49RhrpGak4iHtdyorFmoGqxthqtRSERN/ojZ1gZlHK7RIs99kvtpmxoTfHxLvKb789sORNGz/FkuTgaDhNcnDSvXliujpZgENCRFfmYMTKsFTvw/kXfT337nKFXvG+8ejkx1pGULTf3iaUittJcyDkSWyUVOShaHVDK4bviYCypKZYMxRRW9UDTBd8qOjeKaqTeBLGphFYWrMG7xv1ciCngleHlpeU//fyCv/mrK/74jWW0MpSqShIbes3klJiXzpwk8bnWQq6JUhPWalGM9MZoNV+MgTfHgcMoFDxtzI4s7HgnF13NWT7fiPOtF2GBCL1r1+7agFYK56R/3Wr/rGhSMkforVIy9Ayta3JOfPy08I/fbby8/tEZWtKcf7wN1lpFD4r6QeF9VA+0H/z3Z0fXZ2UAT8USHovpZ1mWPLarEIwAuNu+s1Rqd+OI2h+jNeuS+OabX/H+/Ve8/eqC4fAFV9qxrXfCIXUHnDuhtCPuhPnaHsi5UmKWo2qTvqsYfgqlrpR6h9MOVcUfndLKlgotbSznlW8+VObbwhQtrAdyiWiXmC5HYleQJRytVU3KCaU1zVSq1fgwkPJI1ZqYLbe3iXEUentvBYXGDxYXOtPBcTwMBK94MY346TW9NrxP6HZHS5EP7z9hdePiaqKrTNwaaZ0ZXYfuqVWTq8OHI+PJ4paZkBtKZXrTHI4ek2UX2rtIUDCG+eGMapqsCjYb4qKwXnqXSmvccGA+zzvIpYtdVikUFboieLdHSAuqsNHR1os+tDR0F55vXBI5gbKWcQjEeaPULoOdNbIuMylu1Hp6GkZKvIt8SBSVukaORvFydKylYmplQHTNFQjDhNedoBJGrZJy0Ac6AWssSmuG4wXXr1+jpwO1aloSZxtOcHPK7C2PNsv7pEUpbbQXHJ3WpNKoOYntM63Q4/75UeSYRIMN+06pi4GkyADr431kWTacgcuxczUqxgDTBFcXlsuDYbAKu8eOay0geuuET4DaiziSO9fb7lxroJvhNAy8uc58fxNZU+OhLtRlJiVFVQ7lLb4PcvbsC0pXfIi8eVm4GBK/+dawnC2qDsxpJZnKyVmMduQmSQ2YQeKcchcpE5l5MZjueXGAf/f1NX/zV6/5+rVi9HIjrjQEdVFpJZFSY8lWosyRE3KMmXXdoEsopcmZI4Zr13lxgMNUsE7jbNuh+I1eEpqGU3Ki1saSSqOUhvpBlJM1E8Y4eaFgVwTJLMhYS+2ZnougVtdK2aTA5rLKZ610TN9+XHF91K5KQez7JHIfCv1ecfxcREXQ1PvnwOPPxfOHfdfHQmqevl8pJZxVJTtjafyrp8FarbKjNBhyynw85jYLAAAaPUlEQVS4+cjNwz0pQ1Aj42Qx4SgzMBWwdpKLu4jtsjcp0K01SpKBAkhTu/VCKTM13wHiA88lEWvl4w1snyLrAu+WwFbgbehMVpOjwrbKRRAZV66FosTpUZtkInUPKsjwYbq6RMdKzDBvit4y3hrJri+KaZrwYycchDcQgmZwmqvLkRIjzli865yuNMdrh1EdHyx0wQnSZnKq5O2CtVaaHpmOR4xvXF4NHCaxAz8skO8aqXYo7TMQvNX9nW6UurHFTnBODBMuYI0jHC9YYmabZ5FC2Y59gqIbGb7pvf+tlMiFNEIy61WMGF3aCq03vLcEY2BubLHgt8q6JtZtJcWVkveo9n342dG0DuvtHe9/+Q1DWnk7Wu7nJIDtrvl0XtmaYhgH3KTAFtBZ9ND7NWxdQPuGGweU2MoouZPTQqfirKI3iOuCbo/W7SLMCW334cpj+kYjxlXsk608nerkcZlRaCOvx2OaLiYQa+O8bhgL4+Q4TpbJK0Zfxcp8MJyCx1u3Q5HMk6QR+tPA+FHQqkSSIG22AqpbjoPmq1ee0Vdy8bx57fh057m5T8xJ4llykbgboxVXF4a3rw1ffuWJNXIfMx/nAd0stQdQTiJ5dCevM7lBRaR4g/M477hbEjTHi0nxH3428df//gt++towuChXl6RRisuuNUqtbLmwZUspnSoUT7Y1s86JlCuqV04Ovjxa3p40p2NkOii01QzTDuuvjZ42rJFNWm8CXLfaobWitkhKiUEHtOt7hFOjZaHZWedFFqdFH6Ca2OFrb6hUISZomTEEvrws/PT4OTjg/6u41ipNe6Xk+NSpciTqj8f6HaKh/nkZlWv3se8jz/081FJPP1N6RNKLbTsT1hiz22z3Qr33YPUeFia9W0PukHpj3hLLeSH4iXEa8GaiK40yA8pYet/odaXEe/J2JseFWiK1lp1UtNFbBFWgSeZHTIllqSwlcXdO/MNvIverx1eH05WD64xI72caHKeT5+IU6KFyXyPn1NGHyOHQCYMCoyhUaAmlGrlVHtZGz53NKAH15k4YBqaj4/hKMx0c/uAZwkTXHWs6xsupoamCD52ff32BQuG0pZaVVmdCKJTYeTgnugmE6YgkGW+MwWEOExVLJsP9zLatwMBxuuKcNm7vb+ndkEoScblxNC14PuM9WgexQFqL9l4m+6kRvIYCOipMV7uSoO2XiQwNay+PjmLoCm0UxipKTmQlmuSaGnGrbGtiW2a2uJBSpJQBV6UFotG0LfPuf/2Sb//+7/Bp5cpY/GBYzMjdUjhvnaYU5/uIq40+RKaxo7WjFC1RLkZxeTEyHEeUtVgXUN6jmqJjMVTi9kCJ2x5WJAqB3hu9yrWjEOeOBHGKy09hgS5H2lIxRlokcvztlFKpyqD1QDNyE/KDYTxYhlEzTIqLC8PVKXD0lqCtRDw/JYJIHt1jtPhjdoaCPf9J9LQiGQo407g+Vi6OnlIDpXu27cjDObLEwpLKzjSA4CdeHDUvjpoxdG63zG+v73j/feQcDb1bapOQSEm21bsrsj5mRFJ7Ia6Zy9HyX/70wH/9sxd8+RqCW0FLW7HzWV3UeqHUyMOSuH1ozEthXhbmZeHhYWFZMqU2DgP84vqSv3w58ebCcHnZ8IPD+oEwOkreZIiKAiX24scbj7QOFKq6/TWTWlJb2Rm/BasKTjUe/WXGdnQHbQ3aaUxodK1QTfHGGN5cnbiwP7K4iisLOZLzeWf6+ejPvktU/+y71N4X7Y2nna9SMhCT5z/iDAWyonfS0eehF0+F1pjP+UgiGtC74LuIrrB10jYzn0HrS6yXO49qUFskp0/E87ds59+xbt+zbffUrKUXU8TCqVXa5SMjKSk+vo/85vuV1BVqgzRb7ntgoDPFG4as6BwIAcYLy/Vrj5kKQ6gM1WHnxoAC44RwlDopFTwZnystrvRqaD2wJs2oEd7mNGKGgfE0Mp0MyhnWBA9bZjiOHNyIVZVeV1QvjKGILa8aqCcM4NzKliK1GkbvaTWzzDegMqVptpxRRnP3sHL3sFCTolYBPstuQlx3W5zpvRPGCT8ESitsqTEGTYorp9MRY+Ddu012pCiWmPfhpcXS0O2xuFpJS3AjSkkUumrQZWQron7dGAZDylByJy6ZdYnEVeynrTaJbdEaQ+Xut7/mu7//W3i4wSkDRuFN4xzhfi3kKg6ztCaiBWcUxQk5vndHUxlMJpxOhKOXG4c1KGNwxpNjZVsX1mWVSbFml1lBb4qUMqpFUQZU+XxYI3bIXHZhOnJ019rK3nZPYS0VUu1seePmbgXdmEbLMCqGCU4nzfWFIAaDk7QOrZD0ZNtRposCQWuMbqAyusseRylFbo1KEpUDoHrDm462jvYYd+/hehBjRSmKpqF0CQqdHAxWjsoXfuAXb+H9h1v+z/eGqAOZhRQjkx6YhkDTWfqusYh06+y4CI7/+Ocv+es/O/H22uOcbGDqY0uxlc8zmpbJRcDg90tiiZV5XpnnjXnNpCZ90hdB88dXQVQTY8boijaD2JdrojXRXeOsOEOVkvdkl8s56wje07Eitdo1x1oVnIFgFRqx7jsjPVhLoPVCoqFMpReDS5WTUfgw0pPhX1r/enFVisewYlmPjiWedpSfB1K/797SQubpP3yu9E0e9aYgzi6Jc35MmK08smOt1bv0S+9OFim6FVBNEZxF9wo2U9TCw5oIJUgOD55aM+fzrzl/+jXrfMcWz2xpodaB1keCDVyNGm+THPE2w93DzMdb+O1HjfMDL/rMtS7crhE/HnnpLxhywWvF9UvN5Quwx0Y2IjLvuzmiF8hFEjVzLsxzgxI5KsXU9wFhbITeIDhwljkVdMzEGDhcjZjBsm2J7x7uOPSGnwa06sypEVMnmEwtkThnDNcYO7L2xvu8oVXlqwuDtpWmHgn+lnUrxBJ5mAvzQ8SrkZgzZT1T6AQ30FonZXG5oRWlN0pr2CaqgloLtcrgyhuFbgZvDKULdar0sl8mMowUnWCXF8Zq2PuPsRSq0gwHs+/uHPNSqbWTUmNbC+u6EbcoXn0fMKYTz/d8/NX/Zn7/KygPKA67vroTrCPYQimFUjq5dHqXHXet0Pbil1vGe4M/BcLxiPEjueungVPahL7UasW5gLFQtygs2v3mQGu03UmolAflKLlSSiNX0bBa7wCZV+RYiTEyx427c+a8NuZYMMEyTIZhaBxGuD5ZjqPGmYI1ButEh2usk+QNLTMOtbu7tJIBjtrljDVXGl7sql1mFGgH+2estwKqoE1GlYhuTeK7acS4oapFB0/XDd0zX7wI/MXPr+hEPs6wlEDaMkuBy9MFpkbWFJnrgqLjCPz5T1/yn//kmp9cG5yTHb+g7Y149H8w/G69kErnHDvzVoipk7OYKlIR2MwpWL6+sLw9GabRME4gICUrBhMA5fatfaf0IozmHeBvnZUag9rbK3saRG9oKvoR+QjsOB1RHjmPDgYTCnhwjKT7ex7uCw8PM8v8I3uufT/673N9Pu9e+34z+Dy4+jz9l6/Pz5cjvtaPQw+Z+CvETmqMxlq957SLQBvzSJjve7HtgjbrUHumtErQislJJlFFYcNIzhtpvZGspGJk5zH/ju18R1waMXm2cqISCMMVwzDy+mrA9pklzizLmW2rnHNnsx4dHMsWOUwanxQ5wXRx4OQWnNlw3jGMTuAtSmPoOAWT1qRsqf2Ao5HLQi8ZVaHqhhlHiJX7h4jpEkLXVMcEsQr3Nshxsirs6Di8OODrRt3uuFkb33wXWebCz36imQ6FmCumO4zKfCiRd7Ewqs6rCpfHE95baiucF5jnhQ+3K6VbpvFIWQVGUWsktYpSA7lI+0FrS++Qc9k1sJp1WehlI68JpxWDc1Q0RjWM2wdNSqRBElYpiLv+qC3eqUMK9qOy7Pys1QTvUEp+17aVnX2aSSkTY8KFitOWu3cf+PDrX2LbQlNJ0jFUpzSF0YYxWAoGHGQFuVriZljmFesMbvAcr468+Oqa08vXVDMSi6I0KVpmTxBQquOHgTCOu1ok766xirMjvXdK3glvSowQvRSqIEwEjp0Apfc+eCbnyjxnPt0spKowwTNOgWlonA6KFyfNxaCZnMYZhbFgvdiJhdmqgLLvSBWquR35B1IfOhVAjbssa0M5QEna6WNfVitDU3tY454OoLTFKQtF0Ysm1Y3UIoNz/OlXgckb/vF3C+/vKrd05m0l1IGL04kTjo9qJS2ZSWlOesVXi+K0s0nsnkdlgCqoUGRQXWpjXhsPSyYVTUmdFCs5iSZaKc3rywNfv524PGn84EHl/RQs7ilrjNiJW8coTzOCMrTWiMxOO3oRg4PWe4JrFdC26h2Ml/pkrRhp6Dhr98HahlbIPCScWFfH/XbP/d3K3f2/wUTw+C+KR8E/u23191UA6unYL22Ex16HyJy0dk+DMYXEvqgd8dUaT2YEcUvtKj6hW8hOoSMSClPQHQbtOI2B2jvrqnhtXzJNkPItMd1ye77hl9/8E3ldoFtaHdDmRAGqdjhzpGlN1x6tOjYXelpgc/RecKFhTUNhmazj+qD59rySesUGATesZ0O9MljfCVbzwg240XG1KH79buNmsRQL3Wtq2kBlRtM5HDzVVe7XzBYbpsNAxjhDGBCPfpQj8OE08ObUKed3pPkTd3Pn410nfuicR8MQRNdLP5OpbAVqbrjBM9gRb49Yq2hlI8aVd+8+8P5m43R5jXeeunU5Tmnw1hFjIaeMthazZw2NIaCTpeTE7acPDA5UTWStCHogeIt1oltFNYwVN5vkccmuV/pqsnt1SuJaVFe74L6B11RVKWS2nNHRsq2RbY2kmJ4ig+b7zHe/eUdaFsnC0rKD1jRKhlYLloZXDdc1WllKMdzdStrE6cowOsurN6/54o++RE0TzYwY5bFKs20bqUQMewinPaCNZlskHSBtq0TEO7kmawVlLKVLe0UrtccgVVJZKWVD2UEy7wuUqjHmwDQFXGuYwTEMndOYeHWyXE8io3NGANjGPhoFhB8s88GdOdUkoVgrJQoV6a2hnaJnwU0qXWU3pxqqCpoTpYX8bxraGXToGNcFu4mE9HXAmoCvQkQbbefwNnA5eb79sPLdp5XvPy1s+QZfLrm4nvDmipt3H3hzAV+/Gbg6DhJxs7fztBQHuQt0aRvW2ilFscXOGtnbaImcEq3WXTHUcGROo+Z4CIxBU2umNtn5qlppSjiyvWV6hcEdGMcD6H1z0yTd2RiD6p2cMilHes0oFHawdCNme2sqxkhSgTMDrXe2kuk0zts937675eGhE2Mn9R9LxeIHon/h+PDoSGWf3j8V1j0CRR6UrKS2D6Ss8WgtejDhFDw+dwdlt/w0pX6kgT8Nv3YXijMObXanTIXRW0LQtBxZ71ZKtIzTaXdqGNy88ek+cX+n8OGI0V7ifn2goondseTMkjtDkIb3ViE3yzF4fjZm6vqAdnCpOj8dFHPqOzVKgN7bKqLnq6PFKodxHu8sg9aUDVIpROWxboSqqXEluI6ZDOE0crcllvjAWgy5KfH0W5lo5s3QquFgAqOzrCaQbcCoxKU1bE5R1sT5TrCJx5NF+YG3xtLPhQFwe1upGRHEL7P49a2CtKw0ZXmMOVcF/MHJ41YRDpNwGpS856fjUW6s7YTXjbKJ7Ec3Te8Soy06ZJn8aqQ1VOkSy6Gkt1dLpVlNcB6loLZMXCOuZfBCi9ex0kohxcS6bqzrRs6Ju9uZv/uf/8Cnb/6WN27lZEdcGKhVsYMK8Bomp0mxUWNGD/ZpKJii7J5t0ITLE/gjbrxGuyPaOJSudPWJuCQkZViuvd7Krlj4vGmoZduHrkIAw4jWstN3FUEXaVJrMhhbGzdnWLOl1oy2jsEa3KA5TIVXp8aro+UQPM44KWzO4ayVImsqqmcMnhYryxrpVRGGkTA5upU0ZaV3oJIWdrJgEu3uPnycf4i8UmmHHYbdlFD2GJpGN/KYsx5LoOZKiplRV75+NfH64oo/fnvi033k421k3RTGVcLJ8tqf+Ms/esWf/OTEOHTY9cA0LdFJu165d0WtilY1JWvWqMhNQOwlR2rJO4xaY0j4OjOYE4NzGOqeJrIDeHrdjRxFBtjdos2IUY5UZ0rOOx9i57RajWoK3aDv5iiagJOk3smAMsUZ5TTBGjKGbY6c50KNmZg6n5bKTfyRPFdZj7T3vfBJaZQ36/c0rMIfsPuRXuKuBVlodh7n/pN6I6XIE2mLx3SCuvMH4JGqLna+XQ6mDU2S2xjGwDSAag+s5xviOjOdRrT3WBUYp2umi6/49sMnNjzresay8OLla8bDAMqSWuFh2bj00Ixmo1GpvBgn3HTg7CLmAtgyx6XyF5cHLsdA0GfOayHlzhYDSo3iIFLgVMeGRrw03GzyBmjrOJwmklFYk9HBcbg4McwLd3dnlPWgIdbIkhTDYFDqgZIeWMxr/OipzaD7QCiJY41ob7Ba07JFWUswByzyQVaDiNlbWknJ02thXs6kbWZwhuYNuajduy0qEOHuRnpbqbUT/AuUUiKIbx1rNNYo3OUJ3Qur6jjl0GjW9QFjO0pVce/lRq8F0xSx1F1C52m1i844Zao3eCe62Vwt2wK2WIK1XBw6MQrHc1kX1m1lWRY+fHjP//jv/40hfuD6Z9e0wbHjRNAtYnrDGwXaUoqn1oK1Mg9Ad6xvDKOW1/7iAhWOGHtCKQG4lLTzIkqibEk4k71DFxumAFFEXN73jUftjU7Gaiu7x0eNZteA3fPmNN9/nPnN+43cNePQuLyyDMEzDIbLA1wdDAdn8MZh7YCzA9Z4nJXX3SqNboo0F+6+v+fu0w2lZL748gte//Q12kl/nG7Q7ODqLicIQRvuvRi9h5J39mj7PT2gKlSVlo12la4L3YryoeeKouKtQtvC4C1Xlwdevwjc3a+8/3RmzSveOV5fvOTLFwOH0GRY+bSx6zSqfD3GPXVNr4acG3OEJWZiKrSaBGfZOqp3Xh0tv3g7cgqge4LekZy7RulRKlIP9CrkLoWjpMKqFuktdy3yr7oXoSp92CEMT7MirQU9alSFnmUY3xupiAwsxc62QS2ew3jBwxK5ixvf3S4/sriq3eXxZBpAeqZa70f6zztXGXbJdl9ptf/BOw2rCdZNLs4deNEVxri9FyvtgV7k5z0qBR4VBgrRSkrfTp4zjAem0aFVJ25ncn4AjvtFLna46xdfUP7xlvuHez69f4+ukDL85Cea0U80rbhfEvNpwNmAd5aDETwZXfP6xYnjQXP/8Y4S73jt4DRZUvQYJRP01i3dOLBN+KuqY23n8mB4c63Z8gNJGfzFkdUacnygowij5+XLKx4+nrFV44JCOU03nmY8VVdKLtzPd0zNAwVVLKobBp+pJeOspDBYrahpBZUlhK/ONDS1RdZ0JmbRijptGK1nLQWNwY+WTsN6iVrOMUKVAhm3heAGTBdhUdwW1tY4jB6tNeM07byFhu8Dx4OjtpVlXlG7rTM3iFlo8d6JOsT5QEyF8xYZlcd5RTeibcyxY3Kj9fYEGJnnmfl85u7ull/90zf0eMvbFyOnKdC2QouNlqWHNgSH9lZwhLaibWccPLRGzRFvK+M04qYR7QLaejpKdkptI6UzaV1Iy7pnjml6zfv1IJbVRw+6NdJrL0luTtRCw+3tL9AmC7S6dR7Ole9uNn736YFxDBxOHusVPnQuDp2ryTBZobGpLteW0QNGB6w26F5RRZEeCjffzty+m4nbRteFG3/H5asrxjGI2qM/tlvUzszoSCbUPsySTxFNy9+tukJXA8Wjs0Z1SVtuSkheCoWyCqc8znqBxuuOshVrGpMrXB4stR6xKnD0He+aIDmNESznPsDtfS+s++dY/p9Em8+5UxFjSGuFkiWO6Xo0/Nlrz9cvRgbTUWS5aSgZ8Knd2WnZ24tVaGbdREBaVB1E76r7PjyX9qTI5kSp1FunlEhrBVMSOmictxQSW3qAHujmQG5NcJll5uVBofX044qrUjwNmtgnfeppKKWepFMij3osuPK9ff+DmxIjgHy/vHDGSJ6UDD9E89Z2MfYjbeuxyGolvSeUWOJaLXhtOQ4Kb2UiG3Nj3jKlVEL3MuCwiqurAyEoPtyc9w+FYVsW5od7TpOljY6leOYVXk2WyyGwHBRLWtHO8vLFgcvLkcFBipESQeuy7+QcNiiOJ48fHC6Aqg1NQ5nGMcDbS828wvfLhg0njB65WW7ZlojqjdMxcDg4+v2KNiMYT1OegtnBMoZSG+u6olWjpkJJIvjughCT3aTvVB1Fx9cK1jawHh00KDniWO3QzjF5x7t8g/aN40F2jqVr2TXE/XfSJFa5greGlCJLSoyHK3J23Dy8Zxodg5fsNBGpFWh74qxSeO/Iu1kgxiR9Mz8QgscbwzwvzHHDdwPdELeE1YYQ5PelFmlasz6sxIcH6mB4ETLDz17w5qAYVWfeEqq6p2hl5x15D5ecDnBxfeR4OlFzQVPwVNxgGU8njA9youqFkhdK3ahZ0JWmS2/4sUUl/1T77MBDtxjtKVVcfW3f4SilsbY9ka+MFobDb78/891donuR2fnRYLxmHAyXo+PogFhYz5nsHPraMIwOa+VaNq1S1sjNt3d8/PUDZWmUXmiqcr6dWc8r4VLCOelVIuR7Rhn5bKkuKgL1w8TS3fWomtqt4HKKUU2hCRi7P0+D9R3dLEZb2fXuIYhOV4xtuAFUt6hqsKqhd13zYyGQU6twkfvec+09Q5f2y5YS8yY9Yv0DXKml8eag+fmV5yoYSWdVhW2TYNQwBrTRNBKtV4x29C55bONp57r2LpZlqiR7PKIfaxQt8GMsjpFWpjODgM9LxR+uUcoyz5A2Ry4TuSe0ilyExGkY+Om/wnNV/+/R/nk9r+f1vJ7Xv3X9y6Ou5/W8ntfzel4/ej0X1+f1vJ7X8/oDrOfi+rye1/N6Xn+A9Vxcn9fzel7P6w+wnovr83pez+t5/QHWc3F9Xs/reT2vP8D6vys3UAEGMmchAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5UhqVv9y6QX"
      },
      "source": [
        "## Model 1: Feature extraction transfer learning on 1% of the data with data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isEn6xG11x26",
        "outputId": "c7c457d4-c088-4aff-88eb-68ab3a43f752"
      },
      "source": [
        "# Setup input shape and base model, freezing the base model layers\n",
        "input_shape = (224, 224, 3)\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create input layer\n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "\n",
        "# Add in data augmentation Sequential model as a layer\n",
        "x = data_augmentation(inputs)\n",
        "\n",
        "# Give base_model the inputs (after augmentation) and don't train it\n",
        "x = base_model(x, training=False)\n",
        "\n",
        "# Pool output features of the base model\n",
        "x = layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
        "\n",
        "# Put a dense layer on as the output\n",
        "outputs = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "\n",
        "# Make a model using the inputs and outputs\n",
        "model_1 = keras.Model(inputs, outputs)\n",
        "\n",
        "# Compile the model\n",
        "model_1.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history_1_percent = model_1.fit(train_data_1_percent,\n",
        "                                epochs=5,\n",
        "                                steps_per_epoch=len(train_data_1_percent),\n",
        "                                validation_data=test_data,\n",
        "                                validation_steps=int(0.25 * len(test_data)),\n",
        "                                # Track model training logs\n",
        "                                callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                       experiment_name=\"1_percent_data_aug\")])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/1_percent_data_aug/20210218-040115\n",
            "Epoch 1/5\n",
            "3/3 [==============================] - 10s 3s/step - loss: 2.4697 - accuracy: 0.0371 - val_loss: 2.2468 - val_accuracy: 0.1365\n",
            "Epoch 2/5\n",
            "3/3 [==============================] - 4s 2s/step - loss: 2.1694 - accuracy: 0.1788 - val_loss: 2.1191 - val_accuracy: 0.2533\n",
            "Epoch 3/5\n",
            "3/3 [==============================] - 4s 2s/step - loss: 2.0008 - accuracy: 0.3010 - val_loss: 2.0034 - val_accuracy: 0.3520\n",
            "Epoch 4/5\n",
            "3/3 [==============================] - 3s 2s/step - loss: 1.8276 - accuracy: 0.5748 - val_loss: 1.9212 - val_accuracy: 0.3964\n",
            "Epoch 5/5\n",
            "3/3 [==============================] - 4s 2s/step - loss: 1.6176 - accuracy: 0.6235 - val_loss: 1.8453 - val_accuracy: 0.4375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avfUon5i375o",
        "outputId": "4ce27082-5d3b-4bd6-8815-95a3dbcd787e"
      },
      "source": [
        "# Check out a model summary\n",
        "model_1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (InputLayer)     [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "data_augmentation (Sequentia (None, None, None, 3)     0         \n",
            "_________________________________________________________________\n",
            "efficientnetb0 (Functional)  (None, None, None, 1280)  4049571   \n",
            "_________________________________________________________________\n",
            "global_average_pooling_layer (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 10)                12810     \n",
            "=================================================================\n",
            "Total params: 4,062,381\n",
            "Trainable params: 12,810\n",
            "Non-trainable params: 4,049,571\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wk-edVS14dhQ",
        "outputId": "d54bdbd3-c308-4e94-b175-be3f9e155a5c"
      },
      "source": [
        "# Evalute on the full test dataset\n",
        "results_1_percent_data_aug = model_1.evaluate(test_data)\n",
        "results_1_percent_data_aug"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 117ms/step - loss: 1.8350 - accuracy: 0.4404\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8349741697311401, 0.44040000438690186]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "Tmi0Xu874uTD",
        "outputId": "58f2a30d-a4b5-4c99-c27a-7a814e3b204e"
      },
      "source": [
        "# How do the model with 1% of the and data augmentation loss curves look?\n",
        "plot_loss_curves(history_1_percent)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apcmBOCY5DMF"
      },
      "source": [
        "## Model 2: feature extraction transfer learning model with 10% of data and data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYxfDUfycSoM"
      },
      "source": [
        "# Get 10% of data... (uncomment if you don't have it)\n",
        "# !wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_10_percent.zip\n",
        "# unzip_data(10_food_classes_10_percent)\n",
        "\n",
        "train_dir_10_percent = \"10_food_classes_10_percent/train\"\n",
        "test_dir = \"10_food_classes_10_percent/test\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys03GGLhdQBm",
        "outputId": "3ea1f68e-44cc-4d5c-d329-c56cc6e1a037"
      },
      "source": [
        "# How many images are in our directories?\n",
        "walk_through_dir(\"10_food_classes_10_percent\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_10_percent'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/train'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_curry'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/hamburger'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/sushi'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/grilled_salmon'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ice_cream'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/fried_rice'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/ramen'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/pizza'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/steak'.\n",
            "There are 0 directories and 75 images in '10_food_classes_10_percent/train/chicken_wings'.\n",
            "There are 10 directories and 0 images in '10_food_classes_10_percent/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/steak'.\n",
            "There are 0 directories and 250 images in '10_food_classes_10_percent/test/chicken_wings'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRUisTZTcrgW",
        "outputId": "6782cd91-ae70-4aae-afc9-9e547f0616ec"
      },
      "source": [
        "# Set data inputs\n",
        "import tensorflow as tf\n",
        "IMG_SIZE = (224, 224)\n",
        "train_data_10_percent = tf.keras.preprocessing.image_dataset_from_directory(train_dir_10_percent,\n",
        "                                                                            label_mode=\"categorical\",\n",
        "                                                                            image_size=IMG_SIZE)\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                image_size=IMG_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 750 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fs1R8L36c0uR"
      },
      "source": [
        "# Create model 2 with data augmentation built in\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "# Build data augmentation layer\n",
        "data_augmentation = Sequential([\n",
        "  preprocessing.RandomFlip(\"horizontal\"),\n",
        "  preprocessing.RandomHeight(0.2),\n",
        "  preprocessing.RandomWidth(0.2),\n",
        "  preprocessing.RandomZoom(0.2),\n",
        "  preprocessing.RandomRotation(0.2),\n",
        "  # preprocessing.Rescaling(1./255) # if you're using a model such as ResNet50V2, you'll need to rescale your data, efficientnet has rescaling built-in\n",
        "], name=\"data_augmentation\")\n",
        "\n",
        "# Setup the input shape to our model\n",
        "input_shape = (224, 224, 3)\n",
        "\n",
        "# Create a frozen base model (also called the backbone)\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# Create the inputs and outputs (including the layers in between)\n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "x = data_augmentation(inputs) # augment our training images (augmentation doesn't occur on test data)\n",
        "x = base_model(x, training=False) # pass augmented images to base model but keep it in inference mode, this also insures batchnorm layers don't get updated - https://keras.io/guides/transfer_learning/#build-a-model\n",
        "x = layers.GlobalAveragePooling2D(name=\"global_average_pooling_2D\")(x)\n",
        "outputs = layers.Dense(10, activation=\"softmax\", name=\"output_layer\")(x)\n",
        "model_2 = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "# Compile\n",
        "model_2.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"accuracy\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1T0DTVYgqA1"
      },
      "source": [
        "### Creating a ModelCheckpoint callback\n",
        "\n",
        "The ModelCheckpoint callback intermediately saves our model (the full model or just the weights) during training. This is useful so we can come and start where we left off."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbWM_y85gvMo"
      },
      "source": [
        "# Set checkpoint path\n",
        "checkpoint_path = \"ten_percent_model_checkpoints_weights/checkpoint.ckpt\"\n",
        "\n",
        "# Create a ModelCheckpoint callback that saves the model's weights only\n",
        "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "                                                         save_weights_only=True,\n",
        "                                                         save_best_only=False,\n",
        "                                                         save_freq=\"epoch\", # save every epoch\n",
        "                                                         verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6eMMFGKiTHK"
      },
      "source": [
        "### Fit model 2 passing in the ModelCheckpoint callback"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mmz7gn03i-g3",
        "outputId": "824bc676-3f22-4578-bfcf-0c252902da63"
      },
      "source": [
        "# Fit the model saving checkpoints every epoch\n",
        "initial_epochs = 5\n",
        "history_10_percent_data_aug = model_2.fit(train_data_10_percent,\n",
        "                                          epochs=initial_epochs,\n",
        "                                          validation_data=test_data,\n",
        "                                          validation_steps=int(0.25 * len(test_data)),\n",
        "                                          callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                                 experiment_name=\"10_percent_data_aug\"),\n",
        "                                                     checkpoint_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/10_percent_data_aug/20210218-040152\n",
            "Epoch 1/5\n",
            "24/24 [==============================] - 15s 433ms/step - loss: 2.1584 - accuracy: 0.2149 - val_loss: 1.5152 - val_accuracy: 0.6234\n",
            "\n",
            "Epoch 00001: saving model to ten_percent_model_checkpoints_weights/checkpoint.ckpt\n",
            "Epoch 2/5\n",
            "24/24 [==============================] - 8s 337ms/step - loss: 1.4425 - accuracy: 0.6636 - val_loss: 1.0792 - val_accuracy: 0.7615\n",
            "\n",
            "Epoch 00002: saving model to ten_percent_model_checkpoints_weights/checkpoint.ckpt\n",
            "Epoch 3/5\n",
            "24/24 [==============================] - 9s 354ms/step - loss: 1.0693 - accuracy: 0.7485 - val_loss: 0.8870 - val_accuracy: 0.7911\n",
            "\n",
            "Epoch 00003: saving model to ten_percent_model_checkpoints_weights/checkpoint.ckpt\n",
            "Epoch 4/5\n",
            "24/24 [==============================] - 9s 334ms/step - loss: 0.8986 - accuracy: 0.7862 - val_loss: 0.7616 - val_accuracy: 0.8141\n",
            "\n",
            "Epoch 00004: saving model to ten_percent_model_checkpoints_weights/checkpoint.ckpt\n",
            "Epoch 5/5\n",
            "24/24 [==============================] - 8s 312ms/step - loss: 0.7703 - accuracy: 0.8287 - val_loss: 0.7125 - val_accuracy: 0.8026\n",
            "\n",
            "Epoch 00005: saving model to ten_percent_model_checkpoints_weights/checkpoint.ckpt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCBWmCJ4jh1z",
        "outputId": "c68a1f2c-7480-4e8e-b592-9abe6a776c3e"
      },
      "source": [
        "# What were model_0 results?\n",
        "model_0.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 118ms/step - loss: 0.6042 - accuracy: 0.8416\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6041705012321472, 0.8416000008583069]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlDL8Nb3jxuk",
        "outputId": "c1548392-139e-4a2c-b4c7-69962be1a23f"
      },
      "source": [
        "# Check model_2 results on all test_data\n",
        "results_10_percent_data_aug = model_2.evaluate(test_data)\n",
        "results_10_percent_data_aug"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 118ms/step - loss: 0.6907 - accuracy: 0.8128\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.690684974193573, 0.8127999901771545]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "Y1BKuxsijy4u",
        "outputId": "81280036-0897-4908-ef4c-afb004be6886"
      },
      "source": [
        "# Plot model loss curves\n",
        "plot_loss_curves(history_10_percent_data_aug)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqmM073nkYXO"
      },
      "source": [
        "### Loading in checkpointed weights\n",
        "\n",
        "Loading in checkpointed weights returns a model to a specific checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSRhzYvTlEgl",
        "outputId": "47198467-de33-4378-9180-38f04c224077"
      },
      "source": [
        "# Load in saved model weights and evaluate model\n",
        "model_2.load_weights(checkpoint_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7efc02916898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fF1JPP_nlXoU",
        "outputId": "56b66e1c-77b6-4862-f913-667ecef2696b"
      },
      "source": [
        "# Evalaute model_2 with loaded weights\n",
        "loaded_weights_model_results = model_2.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 118ms/step - loss: 0.6907 - accuracy: 0.8128\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgLtASYtlggH",
        "outputId": "ca1ae8f9-0611-4ca0-935a-45aa86d66d38"
      },
      "source": [
        "# If the results from our previously evaluated model_2 match the loaded weights, everything has worked!\n",
        "results_10_percent_data_aug == loaded_weights_model_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jIHxnxKlp4b",
        "outputId": "48392e0b-a589-4725-8d04-beeaf3afce20"
      },
      "source": [
        "results_10_percent_data_aug"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.690684974193573, 0.8127999901771545]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_H7c0mRlruP",
        "outputId": "15e5c7fe-a066-49ad-d527-9c8907717e60"
      },
      "source": [
        "loaded_weights_model_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6906850337982178, 0.8127999901771545]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrvdLV46lsrK",
        "outputId": "5d8f44db-43e7-4c09-ea0c-0d1772bf1027"
      },
      "source": [
        "# Check to see if loaded model results are very close to our previous non-loaded model results\n",
        "import numpy as np\n",
        "np.isclose(np.array(results_10_percent_data_aug), np.array(loaded_weights_model_results))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwAaI20zmL2b",
        "outputId": "8dc824b7-3172-41ca-9ff2-6607bcec0b64"
      },
      "source": [
        "# Check the difference between the two results\n",
        "print(np.array(results_10_percent_data_aug) - np.array(loaded_weights_model_results))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-5.96046448e-08  0.00000000e+00]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HI46w-xumSXa"
      },
      "source": [
        "## Model 3: Fine-tuning an existing model on 10% of the data\n",
        "\n",
        "> 🔑 **Note:** Fine-tuning usually works best *after* training a feature extraction model for a few epochs with large amounts of custom data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtOKnbyHT7FG",
        "outputId": "886a9eba-f076-4815-cf64-6b168c45193e"
      },
      "source": [
        "# Layers in loaded model\n",
        "model_2.layers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tensorflow.python.keras.engine.input_layer.InputLayer at 0x7efd3182f048>,\n",
              " <tensorflow.python.keras.engine.sequential.Sequential at 0x7efd31dfbeb8>,\n",
              " <tensorflow.python.keras.engine.functional.Functional at 0x7efc844e2940>,\n",
              " <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D at 0x7efc8452e748>,\n",
              " <tensorflow.python.keras.layers.core.Dense at 0x7efc84494dd8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kd3RkHkyVGcg",
        "outputId": "bb16a5e0-a272-427a-f33b-54d2d7092249"
      },
      "source": [
        "# Are these layers trainable?\n",
        "for layer in model_2.layers:\n",
        "  print(layer, layer.trainable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.engine.input_layer.InputLayer object at 0x7efd3182f048> True\n",
            "<tensorflow.python.keras.engine.sequential.Sequential object at 0x7efd31dfbeb8> True\n",
            "<tensorflow.python.keras.engine.functional.Functional object at 0x7efc844e2940> False\n",
            "<tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D object at 0x7efc8452e748> True\n",
            "<tensorflow.python.keras.layers.core.Dense object at 0x7efc84494dd8> True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXEmGWRFVNhS",
        "outputId": "69dd700c-a710-4014-b10f-dc92f1f447ed"
      },
      "source": [
        "# What layers are in our base_model (EfficientNetB0) and are they trainable?\n",
        "for i, layer in enumerate(model_2.layers[2].layers):\n",
        "  print(i, layer.name, layer.trainable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_3 False\n",
            "1 rescaling_2 False\n",
            "2 normalization_2 False\n",
            "3 stem_conv_pad False\n",
            "4 stem_conv False\n",
            "5 stem_bn False\n",
            "6 stem_activation False\n",
            "7 block1a_dwconv False\n",
            "8 block1a_bn False\n",
            "9 block1a_activation False\n",
            "10 block1a_se_squeeze False\n",
            "11 block1a_se_reshape False\n",
            "12 block1a_se_reduce False\n",
            "13 block1a_se_expand False\n",
            "14 block1a_se_excite False\n",
            "15 block1a_project_conv False\n",
            "16 block1a_project_bn False\n",
            "17 block2a_expand_conv False\n",
            "18 block2a_expand_bn False\n",
            "19 block2a_expand_activation False\n",
            "20 block2a_dwconv_pad False\n",
            "21 block2a_dwconv False\n",
            "22 block2a_bn False\n",
            "23 block2a_activation False\n",
            "24 block2a_se_squeeze False\n",
            "25 block2a_se_reshape False\n",
            "26 block2a_se_reduce False\n",
            "27 block2a_se_expand False\n",
            "28 block2a_se_excite False\n",
            "29 block2a_project_conv False\n",
            "30 block2a_project_bn False\n",
            "31 block2b_expand_conv False\n",
            "32 block2b_expand_bn False\n",
            "33 block2b_expand_activation False\n",
            "34 block2b_dwconv False\n",
            "35 block2b_bn False\n",
            "36 block2b_activation False\n",
            "37 block2b_se_squeeze False\n",
            "38 block2b_se_reshape False\n",
            "39 block2b_se_reduce False\n",
            "40 block2b_se_expand False\n",
            "41 block2b_se_excite False\n",
            "42 block2b_project_conv False\n",
            "43 block2b_project_bn False\n",
            "44 block2b_drop False\n",
            "45 block2b_add False\n",
            "46 block3a_expand_conv False\n",
            "47 block3a_expand_bn False\n",
            "48 block3a_expand_activation False\n",
            "49 block3a_dwconv_pad False\n",
            "50 block3a_dwconv False\n",
            "51 block3a_bn False\n",
            "52 block3a_activation False\n",
            "53 block3a_se_squeeze False\n",
            "54 block3a_se_reshape False\n",
            "55 block3a_se_reduce False\n",
            "56 block3a_se_expand False\n",
            "57 block3a_se_excite False\n",
            "58 block3a_project_conv False\n",
            "59 block3a_project_bn False\n",
            "60 block3b_expand_conv False\n",
            "61 block3b_expand_bn False\n",
            "62 block3b_expand_activation False\n",
            "63 block3b_dwconv False\n",
            "64 block3b_bn False\n",
            "65 block3b_activation False\n",
            "66 block3b_se_squeeze False\n",
            "67 block3b_se_reshape False\n",
            "68 block3b_se_reduce False\n",
            "69 block3b_se_expand False\n",
            "70 block3b_se_excite False\n",
            "71 block3b_project_conv False\n",
            "72 block3b_project_bn False\n",
            "73 block3b_drop False\n",
            "74 block3b_add False\n",
            "75 block4a_expand_conv False\n",
            "76 block4a_expand_bn False\n",
            "77 block4a_expand_activation False\n",
            "78 block4a_dwconv_pad False\n",
            "79 block4a_dwconv False\n",
            "80 block4a_bn False\n",
            "81 block4a_activation False\n",
            "82 block4a_se_squeeze False\n",
            "83 block4a_se_reshape False\n",
            "84 block4a_se_reduce False\n",
            "85 block4a_se_expand False\n",
            "86 block4a_se_excite False\n",
            "87 block4a_project_conv False\n",
            "88 block4a_project_bn False\n",
            "89 block4b_expand_conv False\n",
            "90 block4b_expand_bn False\n",
            "91 block4b_expand_activation False\n",
            "92 block4b_dwconv False\n",
            "93 block4b_bn False\n",
            "94 block4b_activation False\n",
            "95 block4b_se_squeeze False\n",
            "96 block4b_se_reshape False\n",
            "97 block4b_se_reduce False\n",
            "98 block4b_se_expand False\n",
            "99 block4b_se_excite False\n",
            "100 block4b_project_conv False\n",
            "101 block4b_project_bn False\n",
            "102 block4b_drop False\n",
            "103 block4b_add False\n",
            "104 block4c_expand_conv False\n",
            "105 block4c_expand_bn False\n",
            "106 block4c_expand_activation False\n",
            "107 block4c_dwconv False\n",
            "108 block4c_bn False\n",
            "109 block4c_activation False\n",
            "110 block4c_se_squeeze False\n",
            "111 block4c_se_reshape False\n",
            "112 block4c_se_reduce False\n",
            "113 block4c_se_expand False\n",
            "114 block4c_se_excite False\n",
            "115 block4c_project_conv False\n",
            "116 block4c_project_bn False\n",
            "117 block4c_drop False\n",
            "118 block4c_add False\n",
            "119 block5a_expand_conv False\n",
            "120 block5a_expand_bn False\n",
            "121 block5a_expand_activation False\n",
            "122 block5a_dwconv False\n",
            "123 block5a_bn False\n",
            "124 block5a_activation False\n",
            "125 block5a_se_squeeze False\n",
            "126 block5a_se_reshape False\n",
            "127 block5a_se_reduce False\n",
            "128 block5a_se_expand False\n",
            "129 block5a_se_excite False\n",
            "130 block5a_project_conv False\n",
            "131 block5a_project_bn False\n",
            "132 block5b_expand_conv False\n",
            "133 block5b_expand_bn False\n",
            "134 block5b_expand_activation False\n",
            "135 block5b_dwconv False\n",
            "136 block5b_bn False\n",
            "137 block5b_activation False\n",
            "138 block5b_se_squeeze False\n",
            "139 block5b_se_reshape False\n",
            "140 block5b_se_reduce False\n",
            "141 block5b_se_expand False\n",
            "142 block5b_se_excite False\n",
            "143 block5b_project_conv False\n",
            "144 block5b_project_bn False\n",
            "145 block5b_drop False\n",
            "146 block5b_add False\n",
            "147 block5c_expand_conv False\n",
            "148 block5c_expand_bn False\n",
            "149 block5c_expand_activation False\n",
            "150 block5c_dwconv False\n",
            "151 block5c_bn False\n",
            "152 block5c_activation False\n",
            "153 block5c_se_squeeze False\n",
            "154 block5c_se_reshape False\n",
            "155 block5c_se_reduce False\n",
            "156 block5c_se_expand False\n",
            "157 block5c_se_excite False\n",
            "158 block5c_project_conv False\n",
            "159 block5c_project_bn False\n",
            "160 block5c_drop False\n",
            "161 block5c_add False\n",
            "162 block6a_expand_conv False\n",
            "163 block6a_expand_bn False\n",
            "164 block6a_expand_activation False\n",
            "165 block6a_dwconv_pad False\n",
            "166 block6a_dwconv False\n",
            "167 block6a_bn False\n",
            "168 block6a_activation False\n",
            "169 block6a_se_squeeze False\n",
            "170 block6a_se_reshape False\n",
            "171 block6a_se_reduce False\n",
            "172 block6a_se_expand False\n",
            "173 block6a_se_excite False\n",
            "174 block6a_project_conv False\n",
            "175 block6a_project_bn False\n",
            "176 block6b_expand_conv False\n",
            "177 block6b_expand_bn False\n",
            "178 block6b_expand_activation False\n",
            "179 block6b_dwconv False\n",
            "180 block6b_bn False\n",
            "181 block6b_activation False\n",
            "182 block6b_se_squeeze False\n",
            "183 block6b_se_reshape False\n",
            "184 block6b_se_reduce False\n",
            "185 block6b_se_expand False\n",
            "186 block6b_se_excite False\n",
            "187 block6b_project_conv False\n",
            "188 block6b_project_bn False\n",
            "189 block6b_drop False\n",
            "190 block6b_add False\n",
            "191 block6c_expand_conv False\n",
            "192 block6c_expand_bn False\n",
            "193 block6c_expand_activation False\n",
            "194 block6c_dwconv False\n",
            "195 block6c_bn False\n",
            "196 block6c_activation False\n",
            "197 block6c_se_squeeze False\n",
            "198 block6c_se_reshape False\n",
            "199 block6c_se_reduce False\n",
            "200 block6c_se_expand False\n",
            "201 block6c_se_excite False\n",
            "202 block6c_project_conv False\n",
            "203 block6c_project_bn False\n",
            "204 block6c_drop False\n",
            "205 block6c_add False\n",
            "206 block6d_expand_conv False\n",
            "207 block6d_expand_bn False\n",
            "208 block6d_expand_activation False\n",
            "209 block6d_dwconv False\n",
            "210 block6d_bn False\n",
            "211 block6d_activation False\n",
            "212 block6d_se_squeeze False\n",
            "213 block6d_se_reshape False\n",
            "214 block6d_se_reduce False\n",
            "215 block6d_se_expand False\n",
            "216 block6d_se_excite False\n",
            "217 block6d_project_conv False\n",
            "218 block6d_project_bn False\n",
            "219 block6d_drop False\n",
            "220 block6d_add False\n",
            "221 block7a_expand_conv False\n",
            "222 block7a_expand_bn False\n",
            "223 block7a_expand_activation False\n",
            "224 block7a_dwconv False\n",
            "225 block7a_bn False\n",
            "226 block7a_activation False\n",
            "227 block7a_se_squeeze False\n",
            "228 block7a_se_reshape False\n",
            "229 block7a_se_reduce False\n",
            "230 block7a_se_expand False\n",
            "231 block7a_se_excite False\n",
            "232 block7a_project_conv False\n",
            "233 block7a_project_bn False\n",
            "234 top_conv False\n",
            "235 top_bn False\n",
            "236 top_activation False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DliFhe6NVm-v",
        "outputId": "7dc01468-8758-4621-9da3-77e3a1c6b76b"
      },
      "source": [
        "# How many trainable varialbes are in our base model?\n",
        "print(len(model_2.layers[2].trainable_variables))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4MwzW3jWFG_"
      },
      "source": [
        "# To begin fine-tuning, let's start by setting the last 10 layers of our base_model.trainable = True\n",
        "base_model.trainable = True\n",
        "\n",
        "# Freeze all layers except for the last 10\n",
        "for layer in base_model.layers[:-10]:\n",
        "  layer.trainable = False\n",
        "\n",
        "# Recompile (we have to recompile our models every time we make a change)\n",
        "model_2.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.0001), # when fine-tuning you typically want to lower the learning rate by 10x*\n",
        "                metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQr77JrvXpxr"
      },
      "source": [
        "> 🔑 **Note:** When using fine-tuning it's best practice to lower your learning rate by some amount. How much? This is a hyperparameter you can tune. But a good rule of thumb is at least 10x (though different sources will claim other values). A good resource for information on this is the ULMFiT paper: https://arxiv.org/abs/1801.06146 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXe0zZBhWslu",
        "outputId": "03e06497-b078-47ba-c13f-9b212cb603c6"
      },
      "source": [
        "# Check which layers are tunable (trainable)\n",
        "for layer_number, layer in enumerate(model_2.layers[2].layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_3 False\n",
            "1 rescaling_2 False\n",
            "2 normalization_2 False\n",
            "3 stem_conv_pad False\n",
            "4 stem_conv False\n",
            "5 stem_bn False\n",
            "6 stem_activation False\n",
            "7 block1a_dwconv False\n",
            "8 block1a_bn False\n",
            "9 block1a_activation False\n",
            "10 block1a_se_squeeze False\n",
            "11 block1a_se_reshape False\n",
            "12 block1a_se_reduce False\n",
            "13 block1a_se_expand False\n",
            "14 block1a_se_excite False\n",
            "15 block1a_project_conv False\n",
            "16 block1a_project_bn False\n",
            "17 block2a_expand_conv False\n",
            "18 block2a_expand_bn False\n",
            "19 block2a_expand_activation False\n",
            "20 block2a_dwconv_pad False\n",
            "21 block2a_dwconv False\n",
            "22 block2a_bn False\n",
            "23 block2a_activation False\n",
            "24 block2a_se_squeeze False\n",
            "25 block2a_se_reshape False\n",
            "26 block2a_se_reduce False\n",
            "27 block2a_se_expand False\n",
            "28 block2a_se_excite False\n",
            "29 block2a_project_conv False\n",
            "30 block2a_project_bn False\n",
            "31 block2b_expand_conv False\n",
            "32 block2b_expand_bn False\n",
            "33 block2b_expand_activation False\n",
            "34 block2b_dwconv False\n",
            "35 block2b_bn False\n",
            "36 block2b_activation False\n",
            "37 block2b_se_squeeze False\n",
            "38 block2b_se_reshape False\n",
            "39 block2b_se_reduce False\n",
            "40 block2b_se_expand False\n",
            "41 block2b_se_excite False\n",
            "42 block2b_project_conv False\n",
            "43 block2b_project_bn False\n",
            "44 block2b_drop False\n",
            "45 block2b_add False\n",
            "46 block3a_expand_conv False\n",
            "47 block3a_expand_bn False\n",
            "48 block3a_expand_activation False\n",
            "49 block3a_dwconv_pad False\n",
            "50 block3a_dwconv False\n",
            "51 block3a_bn False\n",
            "52 block3a_activation False\n",
            "53 block3a_se_squeeze False\n",
            "54 block3a_se_reshape False\n",
            "55 block3a_se_reduce False\n",
            "56 block3a_se_expand False\n",
            "57 block3a_se_excite False\n",
            "58 block3a_project_conv False\n",
            "59 block3a_project_bn False\n",
            "60 block3b_expand_conv False\n",
            "61 block3b_expand_bn False\n",
            "62 block3b_expand_activation False\n",
            "63 block3b_dwconv False\n",
            "64 block3b_bn False\n",
            "65 block3b_activation False\n",
            "66 block3b_se_squeeze False\n",
            "67 block3b_se_reshape False\n",
            "68 block3b_se_reduce False\n",
            "69 block3b_se_expand False\n",
            "70 block3b_se_excite False\n",
            "71 block3b_project_conv False\n",
            "72 block3b_project_bn False\n",
            "73 block3b_drop False\n",
            "74 block3b_add False\n",
            "75 block4a_expand_conv False\n",
            "76 block4a_expand_bn False\n",
            "77 block4a_expand_activation False\n",
            "78 block4a_dwconv_pad False\n",
            "79 block4a_dwconv False\n",
            "80 block4a_bn False\n",
            "81 block4a_activation False\n",
            "82 block4a_se_squeeze False\n",
            "83 block4a_se_reshape False\n",
            "84 block4a_se_reduce False\n",
            "85 block4a_se_expand False\n",
            "86 block4a_se_excite False\n",
            "87 block4a_project_conv False\n",
            "88 block4a_project_bn False\n",
            "89 block4b_expand_conv False\n",
            "90 block4b_expand_bn False\n",
            "91 block4b_expand_activation False\n",
            "92 block4b_dwconv False\n",
            "93 block4b_bn False\n",
            "94 block4b_activation False\n",
            "95 block4b_se_squeeze False\n",
            "96 block4b_se_reshape False\n",
            "97 block4b_se_reduce False\n",
            "98 block4b_se_expand False\n",
            "99 block4b_se_excite False\n",
            "100 block4b_project_conv False\n",
            "101 block4b_project_bn False\n",
            "102 block4b_drop False\n",
            "103 block4b_add False\n",
            "104 block4c_expand_conv False\n",
            "105 block4c_expand_bn False\n",
            "106 block4c_expand_activation False\n",
            "107 block4c_dwconv False\n",
            "108 block4c_bn False\n",
            "109 block4c_activation False\n",
            "110 block4c_se_squeeze False\n",
            "111 block4c_se_reshape False\n",
            "112 block4c_se_reduce False\n",
            "113 block4c_se_expand False\n",
            "114 block4c_se_excite False\n",
            "115 block4c_project_conv False\n",
            "116 block4c_project_bn False\n",
            "117 block4c_drop False\n",
            "118 block4c_add False\n",
            "119 block5a_expand_conv False\n",
            "120 block5a_expand_bn False\n",
            "121 block5a_expand_activation False\n",
            "122 block5a_dwconv False\n",
            "123 block5a_bn False\n",
            "124 block5a_activation False\n",
            "125 block5a_se_squeeze False\n",
            "126 block5a_se_reshape False\n",
            "127 block5a_se_reduce False\n",
            "128 block5a_se_expand False\n",
            "129 block5a_se_excite False\n",
            "130 block5a_project_conv False\n",
            "131 block5a_project_bn False\n",
            "132 block5b_expand_conv False\n",
            "133 block5b_expand_bn False\n",
            "134 block5b_expand_activation False\n",
            "135 block5b_dwconv False\n",
            "136 block5b_bn False\n",
            "137 block5b_activation False\n",
            "138 block5b_se_squeeze False\n",
            "139 block5b_se_reshape False\n",
            "140 block5b_se_reduce False\n",
            "141 block5b_se_expand False\n",
            "142 block5b_se_excite False\n",
            "143 block5b_project_conv False\n",
            "144 block5b_project_bn False\n",
            "145 block5b_drop False\n",
            "146 block5b_add False\n",
            "147 block5c_expand_conv False\n",
            "148 block5c_expand_bn False\n",
            "149 block5c_expand_activation False\n",
            "150 block5c_dwconv False\n",
            "151 block5c_bn False\n",
            "152 block5c_activation False\n",
            "153 block5c_se_squeeze False\n",
            "154 block5c_se_reshape False\n",
            "155 block5c_se_reduce False\n",
            "156 block5c_se_expand False\n",
            "157 block5c_se_excite False\n",
            "158 block5c_project_conv False\n",
            "159 block5c_project_bn False\n",
            "160 block5c_drop False\n",
            "161 block5c_add False\n",
            "162 block6a_expand_conv False\n",
            "163 block6a_expand_bn False\n",
            "164 block6a_expand_activation False\n",
            "165 block6a_dwconv_pad False\n",
            "166 block6a_dwconv False\n",
            "167 block6a_bn False\n",
            "168 block6a_activation False\n",
            "169 block6a_se_squeeze False\n",
            "170 block6a_se_reshape False\n",
            "171 block6a_se_reduce False\n",
            "172 block6a_se_expand False\n",
            "173 block6a_se_excite False\n",
            "174 block6a_project_conv False\n",
            "175 block6a_project_bn False\n",
            "176 block6b_expand_conv False\n",
            "177 block6b_expand_bn False\n",
            "178 block6b_expand_activation False\n",
            "179 block6b_dwconv False\n",
            "180 block6b_bn False\n",
            "181 block6b_activation False\n",
            "182 block6b_se_squeeze False\n",
            "183 block6b_se_reshape False\n",
            "184 block6b_se_reduce False\n",
            "185 block6b_se_expand False\n",
            "186 block6b_se_excite False\n",
            "187 block6b_project_conv False\n",
            "188 block6b_project_bn False\n",
            "189 block6b_drop False\n",
            "190 block6b_add False\n",
            "191 block6c_expand_conv False\n",
            "192 block6c_expand_bn False\n",
            "193 block6c_expand_activation False\n",
            "194 block6c_dwconv False\n",
            "195 block6c_bn False\n",
            "196 block6c_activation False\n",
            "197 block6c_se_squeeze False\n",
            "198 block6c_se_reshape False\n",
            "199 block6c_se_reduce False\n",
            "200 block6c_se_expand False\n",
            "201 block6c_se_excite False\n",
            "202 block6c_project_conv False\n",
            "203 block6c_project_bn False\n",
            "204 block6c_drop False\n",
            "205 block6c_add False\n",
            "206 block6d_expand_conv False\n",
            "207 block6d_expand_bn False\n",
            "208 block6d_expand_activation False\n",
            "209 block6d_dwconv False\n",
            "210 block6d_bn False\n",
            "211 block6d_activation False\n",
            "212 block6d_se_squeeze False\n",
            "213 block6d_se_reshape False\n",
            "214 block6d_se_reduce False\n",
            "215 block6d_se_expand False\n",
            "216 block6d_se_excite False\n",
            "217 block6d_project_conv False\n",
            "218 block6d_project_bn False\n",
            "219 block6d_drop False\n",
            "220 block6d_add False\n",
            "221 block7a_expand_conv False\n",
            "222 block7a_expand_bn False\n",
            "223 block7a_expand_activation False\n",
            "224 block7a_dwconv False\n",
            "225 block7a_bn False\n",
            "226 block7a_activation False\n",
            "227 block7a_se_squeeze True\n",
            "228 block7a_se_reshape True\n",
            "229 block7a_se_reduce True\n",
            "230 block7a_se_expand True\n",
            "231 block7a_se_excite True\n",
            "232 block7a_project_conv True\n",
            "233 block7a_project_bn True\n",
            "234 top_conv True\n",
            "235 top_bn True\n",
            "236 top_activation True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5F7PYfdYR-e",
        "outputId": "8e5816ee-8ada-4c84-e97e-4b61c756ab8f"
      },
      "source": [
        "# Now we've unfrozen some of the layers closer to the top, how many trainable variables are there?\n",
        "print(len(model_2.trainable_variables))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuaGLUcxYf6B",
        "outputId": "47457a4c-3c67-445c-a890-c4908ddbf541"
      },
      "source": [
        "# Fine tune for another 5 epochs\n",
        "fine_tune_epochs = initial_epochs + 5\n",
        "\n",
        "# Refit the model (same as model_2 except with more trainable layers)\n",
        "history_fine_10_percent_data_aug = model_2.fit(train_data_10_percent,\n",
        "                                               epochs=fine_tune_epochs,\n",
        "                                               validation_data=test_data,\n",
        "                                               validation_steps=int(0.25 * len(test_data)),\n",
        "                                               initial_epoch=history_10_percent_data_aug.epoch[-1], # start training from previous last epoch\n",
        "                                               callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                                      experiment_name=\"10_percent_fine_tune_last_10\")])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/10_percent_fine_tune_last_10/20210218-043630\n",
            "Epoch 5/10\n",
            "24/24 [==============================] - 15s 440ms/step - loss: 0.6708 - accuracy: 0.7958 - val_loss: 0.5653 - val_accuracy: 0.8141\n",
            "Epoch 6/10\n",
            "24/24 [==============================] - 8s 315ms/step - loss: 0.5688 - accuracy: 0.8179 - val_loss: 0.5274 - val_accuracy: 0.8224\n",
            "Epoch 7/10\n",
            "24/24 [==============================] - 8s 327ms/step - loss: 0.4763 - accuracy: 0.8441 - val_loss: 0.5024 - val_accuracy: 0.8273\n",
            "Epoch 8/10\n",
            "24/24 [==============================] - 8s 309ms/step - loss: 0.4588 - accuracy: 0.8588 - val_loss: 0.5075 - val_accuracy: 0.8257\n",
            "Epoch 9/10\n",
            "24/24 [==============================] - 8s 298ms/step - loss: 0.3706 - accuracy: 0.8875 - val_loss: 0.4598 - val_accuracy: 0.8470\n",
            "Epoch 10/10\n",
            "24/24 [==============================] - 8s 322ms/step - loss: 0.3420 - accuracy: 0.8954 - val_loss: 0.4826 - val_accuracy: 0.8438\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXRpgJySbImo",
        "outputId": "b88f3780-d0e3-4bec-f3ff-7b5dc642440d"
      },
      "source": [
        "# Evaluate the fine-tuned model (model_3 which is actualy model_2 fine-tuned for another 5 epochs)\n",
        "results_fine_tune_10_percent = model_2.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 118ms/step - loss: 0.4812 - accuracy: 0.8416\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "F9AdryHcbvsI",
        "outputId": "34f63aa4-f1d9-4b98-ea20-512191fc74d6"
      },
      "source": [
        "# Check out the loss curves of our fine-tuned model\n",
        "plot_loss_curves(history_fine_10_percent_data_aug)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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duzNw4MCLfhsuICCAuXPn/uF527dv5+WXX2b16tWEhYWdbgL28MMPc9111zF37lyKi4vJycm5aM/4goICTrWSOHbsGGvXrkVEmDx5Mq+99hpvvPFGqX3ofX19+fe//81///tffH19mTJlCh999NGV/vqUcgmHs/J4YtYmVsWnc22zWrx2WzvqBldOC4OK5HpBf5GRd0Xp2LEjqampHDx4kLS0NGrUqEHdunV59NFHWblyJV5eXqSkpHDkyBHq1q17wW0ZY3jmmWf+8Lxly5YxePDg01eNOtW/fdmyZad7tnt7exMcHHzRoD/VsAysi4QMHTqUQ4cOUVBQcLof/fn60Pfu3Zv58+fTsmVLCgsLadu27SX+tpRyLcYY5m06yL++3UphseGlW9owsluU07QwuFKuF/QONHjwYGbNmsXhw4cZOnQoX375JWlpacTFxeHr60t0dHSZ+r5f7vNK8vHxwWaznb5/oX7x48aN47HHHmPgwIGsWLGCCRMmXHDb9957L6+88gotWrTQNsLK7R07UcCz323lh82H6BgVwptDOtAwLOjiT3Qhzn8CqBMZOnQo06dPZ9asWQwePJisrCxq166Nr68vy5cv58CBA2Xazvme17t3b2bOnElGRgZwpn97nz59mDhxIgDFxcVkZWVRp04dUlNTycjIID8/n/nz519wf6f6xX/22Wenl5+vD323bt1ISkriq6++Yvjw4WX99SjlcpbvSuWGt1ayaOthnvhzc2b+9Sq3C3nQoL8krVu3Jjs7m/DwcOrVq8eIESOIjY2lbdu2TJs2jRYtWpRpO+d7XuvWrfnnP//JddddR/v27XnssccAePvtt1m+fDlt27alc+fObN++HV9fX5577jm6du1K3759L7jvCRMmMHjwYDp37nzWxcTP14ceYMiQIfTo0aNMlxVUytWcyC/imblbGDNlPTWq+PLtgz14sFcTfCr4It2OUqZ+9JVJ+9E7hwEDBvDoo4+W2h2zMujfXFWUuANHeWzGJhKPnuS+PzXisb7NCPCtvOu3VpQL9aN3z7cvddkyMzNp1qwZgYGBDgt5pSpCflEx/1m4k8EfrqHYZph+X3ee6d/SLUL+YvRgbAXasmULo0aNOmuZv78/v/32m4MquriQkBB2797t6DKUKlc7Dh3n0W82svNwNkNjInl2QEuqBTju0n6VzWWC3hjjcqc6tW3bVvvFXwZnm05UrqvYZpi0ai9vLt5N9UAfJt8Zw/Wt6ji6rErnEkEfEBBARkYGoaGhLhf26tIYY8jIyCAgwPW/pKIcKzHjJI/P3Mj6/cf4c+s6vPKXtoRW9Xd0WQ7hEkEfERFBcnIyaWlpji5FVYKAgAAiIiIcXYZyUcYYpq9P4qX52/EW4c0h7flLx3CPHiS6RND7+vqe/janUkqdT2p2HuNnb2HZzlSubhzKfwe3Jzwk0NFlOZxLBL1SSl3Mgi2H+OfcLZwsKOb5m1tx11XReDnppf0qmwa9UsqlZeUW8vx3W/l240HaRQTz5pD2NKldzdFlORUNeqWUy/olPp0nZm0iNTufv1/flAd7NcHXTb/deiU06JVSLie3wPry09TV+2lUK4g5D1xN+8gQR5fltDTolVIuZWNSJo99s5G96ScY0yOap/q18Ihvt14JDXqllEsoLLbx7rIE3l+eQO1q/nx5bzd6NAm7+BOVBr1SyvnFH8nm0Rkb2ZpynFs7hfP8za0JDvScFgZXSoNeKeW0bDbDp7/u47VFu6jq78OHIzvRr009R5flcjTolVJOKfnYSf4xcxNr9x6lT4va/N9tbaldTVtjXA4NeqWUU8k6WcjMuCTeXhKPzRj+c1tbhsREenQLgyulQa+UcjhjDL/tO8r0dYks2HqYgiIbVzUK5bXb2xFZs4qjy3N5GvRKKYdJz8lndlwy36xPYm/6CaoF+DA0JpKhXSJpEx7s6PLchga9UqpS2WyGVQnpTF+XyE/bj1BkM3SJrsGDvZrQv209Av30nPjypkGvlKoUh7JymRlrjd5TMnOpUcWX0VdHM6xrpPamqWAa9EqpClNUbGPZzlSmr09ixa5UbAauaRLG0/1b0LdVHfx9dPReGTTolVLlLjHjJN/EJjIzNpnU7HxqV/PngZ6NGRoTRVSoHlytbBr0SqlykV9UzOJtR5i+PpFfEzLwEujVvDbDukbRq3ktfLSrpMNo0CulrkhCajZfr0tizu/JHDtZSHhIII/1bcbgmAjqBevVnZyBBr1S6pLlFhTzw5ZDTF+XSOyBY/h6C31b1WFYlyiuaRKmV3ZyMhr0Sqky25qSxfT1iXy34SDZ+UU0Cgvimf4tuLVTBGFV/R1dnjoPDXql1AVl5xUyb9NBpq9LYktKFv4+XvRvW49hXSLp2rCmtiZwARr0Sqk/MMbwe2Im09clMn/zIXILi2lRtxovDGzNLR3CCa6iLYJdSZmCXkT6AW8D3sBkY8yr5zweBXwGhNjXGW+MWWB/rB3wEVAdsAFdjDF55fYKlFLl5tiJAuZsSOGb9YnsPpJDFT9vBnWoz7CuUbSPCNbRu4u6aNCLiDfwPtAXSAbWi8g8Y8z2Eqs9C8wwxkwUkVbAAiBaRHyAL4BRxphNIhIKFJb7q1BKXTabzbB2XwbT1yWxcJvVUKx9ZAiv3tqWAe3rU9VfP/i7urL8BbsCCcaYvQAiMh0YBJQMeoM1YgcIBg7ab98AbDbGbAIwxmSUR9FKqSuXmp3HLHtDsQMZJ6ke4MPwLpEM6xpFy3rVL74B5TLKEvThQFKJ+8lAt3PWmQAsFpFxQBBwvX15M8CIyCKgFjDdGPPauTsQkbHAWICoqKhLqV8pdQmKbYaV8WlMX5fI0h2pFNkMXRvW5O/XN+XGNvX0Ittuqrw+kw0Hphpj3hCRq4DPRaSNffvXAF2Ak8BSEYkzxiwt+WRjzMfAxwAxMTGmnGpSStmlZOYyY30SM2OTOJiVR2iQH3df05ChXSJpXKuqo8tTFawsQZ8CRJa4H2FfVtI9QD8AY8waEQkAwrBG/yuNMekAIrIA6AQsRSlVoQqLbSzdcYTp65P4eXcaYDUUe3ZAK65vWQc/H21J4CnKEvTrgaYi0hAr4IcBd5yzTiLQB5gqIi2BACANWAQ8KSJVgALgOuB/5VS7UqoU+9NPMH19ErPikknPyadu9QDG9WrC4JhIvVqTh7po0BtjikTkIazQ9gY+NcZsE5EXgVhjzDzgcWCSiDyKdWB2tDHGAMdE5E2sNwsDLDDG/FBRL0YpT5VXWMyibYf5el0ia/cexdtL6NW8NsO7RnJdM20o5unEymPnERMTY2JjYx1dhlIuYfeRbL5el8jcDSlkniwksmYgw7pEcXvnCOpUD3B0eaoS2Y9/xpT2mJ4gq5SLOVlQxPxNh/h6fSIbEjPx9RZuaF2X4V2iuLpxqDYUU3+gQa+UCzDGsCUli6/XJfH9poPk5BfRuFYQz97Ukls7RVAzyM/RJSonpkGvlBMzxvD95kN8uGIP2w8dJ8DXaig2vGsUMQ1qaEsCVSYa9Eo5qe0HjzPh+22s23eU5nWq8dKg1gzsEE5woDYUU5dGg14pJ5N5soA3f9rNF2sPEBzoyyt/acvQLpF469y7ukwa9Eo5iWKb4Zv1Sfx30U6ycgsZ2b0Bj/VtRkgVnX9XV0aDXiknEHfgGBPmbWNLShZdo2syYWBrWtXXxmKqfGjQK+VAqdl5/OfHXcz+PZk61f15e1gHBravrwdZVbnSoFfKAQqLbXy2ej9vLYknv6iY+69rzLjeTQjS3u+qAuj/VUpVsl/i05nw/TYSUnPo2bwWzw1oRSPtIKkqkAa9UpUk+dhJXp6/g4XbDhNVswqT74yhT8vaOk2jKpwGvVIVLK+wmA9/3sPEFXsQgX/c0Ix7/9RIL/KhKo0GvVIVxBjD4u1HeGn+dpKP5XJTu3r8s39L6ocEOro05WE06JWqAAmpObzw/TZWxafTrE5VvrqvG1c3DnN0WcpDadArVY5y8ot4Z2k8n/6yj0A/b54b0IpRVzXAV/vBKwfSoFeqHBhj+HZjCv+3YCep2fkMiYngyX4tCKvq7+jSlNKgV+pKbU3JYsK8bcQeOEb7iGA+GtWZjlE1HF2WUqdp0Ct1mY6dKOD1xbv4al0iNav48Z/b2jK4c6Re+EM5HQ16pS5Rsc3w1bpE3li8i+y8Iu66KppH+zbT9sHKaWnQK3UJYvcf5bnvtrH90HG6N7Kaj7Woq83HlHPToFeqDFKP5/F/P+5k7oYU6gUH8N4dHbmpbT39VqtyCRr0Sl1AQZGNKb/u452l8RQWGx7q1YS/9WpMFT/9p6Nch/7fqtR5/Lw7jRe+38betBP0aVGbfw1oRXRYkKPL8gwHVsOhTRAcAcGREBIFgTVAP0FdFg16pc6RdPQkL87fzk/bjxAdWoUpo7vQq0VtR5flGWw2WPU6LH8FMGc/5hsEIZH24I888wZw6n7VuuClX0wrjQa9Una5BcVM/HkPH/68Bx8v4cl+zbnnmob4+2jzsUqRlwVz/gq7f4R2Q+H6CZCTCllJkJlk/2+i9d+UOMg9evbzvXytTwAhkRAc9cc3herh4OOZl2XUoFcezxjDwq2HefmHHaRk5jKwfX2e7t+CesHafKzSpO6A6SMg8wDc+Bp0HWtN01SvD/U7lP6c/BzISraHf2KJN4Mk2LMUsg9z9qcCgWr1LvypwM89p+Y06JVHiz+SzYTvt/FrQgYt6lZj+tjudG8U6uiyPMvWOfDdQ+BfFe6aDw2uKtvz/KtC7RbWT2mK8uF4ytlvAKc+ESSvh+3fgq3o7OdUCS3xJlDKpwIXPU6gQa88UnZeIW8viWfq6v1U8fPmhYGtGdEtCh9tPlZ5iotg6QRY/S5EdoPBn0H1euW3fR9/qNnI+imNrdga9Z+eGirxqSBtN8QvgaLcs5/jV/WcTwMlPhWEREFQbac8TqBBrzyKzWaYsyGFV3/cScaJfIZ1ieQfNzQnVJuPVa4T6TBzNOxfBV3uhT//X+XPn3t5Q3C49RPV/Y+PGwMnM858Cij5ySArEZLWQV7m2c/x9itxplApnwqqh4N35X+DWoNeeYwtyVk8N28rGxIz6RgVwqejY2gXEeLosjxPShx8cyecTIdbJkKHOxxdUelEICjM+gnvVPo6ecet4wSnDhSXfFOI/wlyjpyzTS/rOMEfPhWUeEPwq1LuL0WDXrm9oycK+O+inUxfn0RokB//vb0dt3WK0OZjjvD7NPjhcetUyLsXnf9Aq6sIqA4BraBOq9IfL8yzHyc491NBIiT+BsfngCk+s36LATDsy3IvU4Neua2iYhtfrUvk9UW7OFlQzN09GvLI9U2pHqDNxypdUT78+CTETYVGveD2T6FKTUdXVfF8AyC0sfVTmuIiyD505k2gaq0KKUODXrml3/Zm8Py8bew8nE2PJqFMuLk1TetUc3RZnikrBWaMsqZsrnkMej9rzY8r8PaxpmxCIqFBxe1Gg165lcNZebyyYAfzNh0kPCSQiSM60a9NXW0+5ij7VsGsMVCYC0M+h1YDHV2RR9KgV24hv6iYT37Zx3vLEiiyGR7u05QHrmtMoJ+OHB3CGFj7ASz+lzVtMfoHqNXc0VV5LA165fKW70zlxfnb2Zd+ghta1eFfA1oRWbP8z1xQZVRwAuaNg62zrYOLt0y0DloqhynTmf0i0k9EdolIgoiML+XxKBFZLiIbRGSziPQv5fEcEflHeRWu1IGME9z72XrGTF2PAJ/d3ZWP74zRkHekjD0w+XrYNhf6PA9Dv9CQdwIXHdGLiDfwPtAXSAbWi8g8Y8z2Eqs9C8wwxkwUkVbAAiC6xONvAj+WW9XKo50sKOKD5Xv4eNVefL2Ep29swZgeDfHzcb5vJHqU3Ytg9n3WN0NHzIImfRxdkbIry9RNVyDBGLMXQESmA4OAkkFvgFNv28HAwVMPiMgtwD7gRHkUrDxXYbGNbzek8L+fdnMwK4+/dAxn/I0tqFM9wNGleTabDVa+BitehbptrVF8jQo8hURdsrIEfTiQVOJ+MtDtnHUmAItFZBwQBFwPICJVgaewPg2cd9pGRMYCYwGioqLKWLryFAVFNub8nsz7KxJIOppL2/Bg3h7ekS7RHnAetrPLzYS5f8iUhJQAABjeSURBVIXdC6H9cBjwP/DVrp/OprwOxg4Hphpj3hCRq4DPRaQN1hvA/4wxORc6vc0Y8zHwMUBMTIw574rKo+QXFTMzNpmJK/aQkplL+4hgXhjYml7Na+vpks7gyDb4ZqT1Lc/+r1s9a/Tv4pTKEvQpQGSJ+xH2ZSXdA/QDMMasEZEAIAxr5H+7iLwGhAA2Eckzxrx3xZUrt5VXWMw365OYuGIPh4/n0SkqhFdubcu1TcM04J3FllnWmTX+1a1TJ0trCqacRlmCfj3QVEQaYgX8MODcLkSJQB9gqoi0BAKANGPMn06tICITgBwNeXU+uQXFfPnbAT5euZfU7Hy6RtfkjSHtubpxqAa8sygugiXPw5r3ILI7DPkMqtV1dFXqIi4a9MaYIhF5CFgEeAOfGmO2iciLQKwxZh7wODBJRB7FOjA72hijUzCqTE7kF/HF2gNMWrWX9JwCrm4cyjvDO+oFQJxNTpr1Ldf9q6DrX+GGlz320nyuRpwtj2NiYkxsbKyjy1CVIDuvkGlrDjB51V6OnSzkT03DeLhPUz3I6oySY2HGnVZ/9pvfhvbDHF2ROoeIxBljYkp7TL8ZqypdVm4hn63ezye/7CMrt5BezWsxrk9TOkXVcHRpqjRxU2HBE9YUzT2LoV57R1ekLpEGvao0mScL+PTX/Uz5dR/ZeUVc37IOD/dpohf/cFZF+bDgH1YP+cZ94LbJntFa2A1p0KsKd/REAZ/8spfPVh8gJ7+Ifq3r8lDvJrQJD3Z0aep8spLhm1Fw8Hf40z+g1zPaWtiFadCrCpOek8+kVXv5fM0BcguL6d+2HuN6N6FFXe194tT2rYSZY6wR/dAvoeUAR1ekrpAGvSp3qcfz+HjlXr747QAFRTZubl+fh3o10Qt/ODtjrNMmf3oeQptYl7QLa+roqlQ50KBX5eZwVh4f/ryHr9clUmQzDOpgBXyjWlUdXZq6mPwcmPeQ1XWy5UC45QPw1zdmd6FBr65YSmYuH67Ywzfrk7AZw62dwvlbzyZEhwU5ujRVFhl7YPoISN8F178APR7RVgZuRoNeXbakoyf5YMUeZsVZPe8Gx0TywHWNtR+8K9n1I8wZC14+MHIONO7l6IpUBdCgV5dsf/oJ3l+ewJwNKXiLMLxrFPdf15j6Idq10GXYbPDzq/Dzf6zz4od+ASHaOdZdadCrMtuTlsP7yxP4buNBfLyEO69qwF+vbUzdYO0H71Jyj1mj+PjF0GEE3PSGthZ2cxr06qLij2Tz7rIE5m8+iJ+PF3f3iOa+axtRu5oGvMs5vBW+GQFZKXDTmxBzt87HewANenVeOw4d571lCSzYeohAX2/GXtuYe//UkLCq/o4uTV2OzTOt1sKBITBmAUR2dXRFqpJo0Ks/2JqSxbvL4lm07QhV/X14sGcT7r6mITWDtFOhSyouhJ+eg7UfQNTVMHgqVKvj6KpUJdKgV6dtSsrk3WXxLNmRSrUAHx7p05S7ezQkuIqvo0tTlysnFWaOhgO/QrcH4IaXwFv/np5Gg14Rd+AY7y6LZ8WuNIIDfXm8bzPu6hFN9QANBJeWtB5mjLKu63rrJGg3xNEVKQfRoPdg6/cf5Z2l8ayKT6dmkB9P9mvOnVdFU9Vf/7dwacZA3BRY8CQEh8O9P0Hdto6uSjmQ/ov2MMYY1u61An7N3gzCqvrxTP8WjOjWgCANeNdXmAcLHocNX0CTvnDbJAjUPv+eTv9lewhjDL8mZPDO0njW7T9KrWr+/GtAK+7oGkWgn7afdQuZSdZUzcENcO2T0HO8thZWgAa92zPG8PPuNN5ZGs/viZnUCw7ghYGtGdolkgBfDQG3sXcFzLrbOsNm+HRofqOjK1JORIPeTRljWLojlXeWxbM5OYvwkED+/Zc23N45An8fDXi3YQysfgeWTICw5lYrg7Amjq5KORkNejdjsxkWbz/Cu8vi2XbwOJE1A/nPbW35S8cI/Hy8HF2eKk/52fDdQ7D9W2j9Fxj4HvhrS2j1Rxr0bsJmM/y49TDvLotn5+FsokOr8Prg9gzqUB9fbzcK+IKTkHMY/KpBQDD4eOiXuNITrFYG6buh70tw9ThtZaDOS4PexRXbDPM3H+S9ZQnEp+bQuFYQbw3twIB29fBx5YA3Bo6nWL1ZjmyBI9us20f3gLGdWc8nwAr8gGDwr26/Xf2c+8HnX8evqusF5M4fYO791hefRn0Lja5zdEXKyWnQu7DtB4/z0Ne/szftBM3qVOXd4R3p37Ye3l4uFlyFuZC6A45stQf7Nut2XuaZdUIaWOeCt7nVul1wAvKzIC8L8o7b/5tlPSfzwJllxfkX3rd42cP/1JtDyTeGc98wSnkD8a9eeZ8qbMWw4v9g5X+hfkcY8jmERFbOvpVL06B3UfvST3Dnp7/h4+XFxBGd+HPrung5e8CfGqUf2QaHt1hhfmQbZCScGaX7BkGdVtacc53WVrjXbmWF7OUozIP842eC/9w3h/zjf3yzyDxwZln+ccBceB++VS7wZlDyTeE8byB+QRf/VHHyKMy5DxKWQMdR0P918NXuoapsNOhd0OGsPEZO/g2bgS/u7UaT2k54AO70KH1biZF6KaP0Om3OhHqdNlCjIXiV45STb4D1U7X25T3fZoOC7Au/OZx+87AvO3kUju0/s6y44ML7EG8r/M871VQdNn9jtRYe8BbEjLm816I8lga9i8k8WcCdn/5G5skCvh7b3fEhbwwcP2gfnZcI9LNG6VWsUXnrW6wwr9PGGrUHBDu29rLw8joTupfr9KeKU28OmaW8WRw/+83i6L4zy/KPQ3AkjPkRIruU32tTHkOD3oWcyC9i9JT17E8/ydQxXWgXEVK5BRTmQdqOs+fRj2y1rlh0SkiUFeStboG6bSpmlO5qrvhTRTEgnv07VFdEg95F5BcVc/8XcWxOzuSDEZ25uklYxe3MGMg+9MczXjISwBRb65wapbccaM2ju9Io3dVoGwN1hTToXUCxzfDYjE2sik/ntdva0a9N3fLbeGEepO08e9rl3FF6cJQ1Om810Ar0um2hRrQGkFIuQoPeyRlj+Nd3W/lh8yGe6d+CIV0u83S6U6P0c894SY8/M0r3CbRG5S1PBXoba9QeWMlTREqpcqVB7+TeWLybr35L5P7rGjP22sZle9LpUfqpM17s0y+5R8+sExxlnenS8mb7GS9toWZDHaUr5YY06J3Y5FV7eW95AsO6RPJUv+alr5R9+MwI/dTUy7mj9NotoeUAK8zrtLZ+dJSulMfQoHdSs+OSefmHHdzYpi7//ktb5NQXamzFkBIHuxbAroXWWTCnBEdaId5iwJkzXmo20lG6Uh5Og94J/bT9CE/O3kyPJqG8NawD3kUnYc9y2PUjxC+CE2nWl2waXA0dX7a+Dl+ntV5JSClVKg16J7N2bwYPfvU719YpYGKbrfh/8wbs/dnq2eIfDE2vh+b9oUkfDXalVJmUKehFpB/wNuANTDbGvHrO41HAZ0CIfZ3xxpgFItIXeBXwAwqAJ4wxy8qxfvdhDAlb1hA7ewrf+/1O82MJsBCrTUDM3dYVgxpcbXUsVEqpS3DRoBcRb+B9oC+QDKwXkXnGmO0lVnsWmGGMmSgirYAFQDSQDtxsjDkoIm2ARUB4Ob8G11WUD/tXwa4fKdqxgCY5B/mbCEV1OkOr56yRe60WrtdGVynlVMoyou8KJBhj9gKIyHRgEFAy6A1wqr1gMHAQwBizocQ624BAEfE3xlykd6wbO5EB8Yutg6l7lkFBDjafQH61tWOFDGL06L/SoEFDR1eplHIjZQn6cCCpxP1koNs560wAFovIOCAIuL6U7dwG/F5ayIvIWGAsQFRUVBlKcjHp8fazZH6EpN+sZl9V60Lb28lu0JdhP/lx4Lhh+tjuNAjXFgJKqfJVXgdjhwNTjTFviMhVwOci0sYYq32hiLQG/gPcUNqTjTEfAx8DxMTEXKT5twsoLrIC/VS4H91jLa/bFv70D2u+vV4HThTaGDn5N+KPHeezMV1poyGvlKoAZQn6FKDk9+4j7MtKugfoB2CMWSMiAUAYkCoiEcBc4E5jzJ4rL9lJ5R2HPUvtp0AutnrFePlCw2uh+wPQrN9ZVwPKLyrmr5/HsTUli4kjOnFV41AHFq+UcmdlCfr1QFMRaYgV8MOAO85ZJxHoA0wVkZZAAJAmIiHAD1hn4fxafmU7icxE60tLuxbA/l/AVmid8tisn/XTuHepV0Yqthke/WYjvySk8/rg9tzQuhyblCml1DkuGvTGmCIReQjrjBlv4FNjzDYReRGINcbMAx4HJonIo1gHZkcbY4z9eU2A50TkOfsmbzDGpFbIq6loNhsc2mCN2nf9aLUbAAhtCt3vt86SiegK3uf/tRpjePbbLSzYcphnb2rJ7Z0jKql4pZSnEmOca0o8JibGxMbGOrqMMwpzrS8s7VoAuxdBzmHrgtJRV1mj9uY3QljTMm/utYU7+WDFHh7s1Zgn/tyiAgtXSnkSEYkzxsSU9ph+M7Y02UesVgO7frRaDxTlgl8169uozW+EpjdAlZqXvNlJK/fywYo93NEtin/ccJ4mZUopVc406MHq1Z6648xZMin2TxTBkdBxpBXu0deAj/9l72JGbBL/XrCDm9rW46VBbc40KVNKqQrmuUFfVACJq+3z7QusA6sA9TtBr39a4V6nTbl8K3XRtsOMn72ZPzUN482h7fH20pBXSlUezwr63GMQv8QK9oQlkH8cfAKgUU+45jFrzr16vXLd5eo96Yz7egPtIkL4cGRn/H20ZbBSqnK5f9Bn7IHdC62R+4HV1gU5gmpBq0HWqL1RT/ALqpBdb0nOYuy0OBrUrMKU0V0I8nf/X7dSyvm4X/LYiiE59sx8e/oua3ntVnDN36HZjRDeGby8KrSMPWk53DVlHcGBvnx+TzdqBPlV6P6UUup83CfoM/bAqjes0fvJDPDygQY97C1++0GN6Eor5WBmLqMm/4aXwBf3dqNucECl7Vsppc7lPkEvAjvnW6c+NusHTa53yHVRj54oYNQnv5GdV8TXY7vTMKxipoWUUqqs3CfoazaCJ/Ze8FupFS0nv4jRU9aRfCyXaXdrkzKllHNwn6AHh4Z8flExY6fFsu3gcT4a2ZlujbRJmVLKOVTsEUkPUVRs45GvN7J6Twb/vb0d17eq4+iSlFLqNA36K2SM4Z9zt7Jw22GeG9CKWztpkzKllHPRoL9Cry7cyTexSYzr3YS7r9FLACqlnI8G/RX48Oc9fPTzXkZ2j+Kxvs0cXY5SSpVKg/4yfbM+kVd/3MmAdvV4YaA2KVNKOS8N+suwcOshnp6zhWub1eLNIR20SZlSyqlp0F+iXxPSefjrjXSIDOHDkZ3w89FfoVLKuWlKXYJNSZmMnRZLw7AgPh3dhSp+7vU1BKWUe9KgL6OE1GxGT1lHzap+TLunKyFVtEmZUso1aNCXQUpmLqM+WYe3lxef392NOtW1SZlSynVo0F9ERk4+oz75jZz8Iqbd3ZVobVKmlHIxGvQXkJ1XyOgp60k5lsuno7vQqn51R5eklFKXTIP+PPIKixk7LY4dh44zcWQnukTXdHRJSil1WfS0kVIUFdt4+OsNrNmbwVtDO9C7hTYpU0q5Lh3Rn8MYw9NztrB4+xEm3NyKWzqGO7okpZS6Ihr0JRhjeGXBDmbGJfNIn6aM7qFNypRSrk+DvoSJP+9h0qp93HVVA/5+fVNHl6OUUuVCg97u63WJvLZwFwPb1+f5m1trkzKllNvQoAcWbDnEP+duoWfzWrwxpD1e2qRMKeVGPD7oV8Wn8cj0DXSKqsHEEZ3x9fb4X4lSys14dKptSDzGXz+Po3GtqnxyVxcC/bwdXZJSSpU7jw36+CPZjJm6nrCq/ky7uyvBVXwdXZJSSlUIjwz65GMnGfXJOny9vfjinm7U1iZlSik35nFBn56Tz6hP1nGywGpSFhVaxdElKaVUhfKoFgjH8wq569N1HMrK5Yt7utGynjYpU0q5P48Z0ecVFnPvZ7HsOpzNxJGdidEmZUopD1GmoBeRfiKyS0QSRGR8KY9HichyEdkgIptFpH+Jx562P2+XiPy5PIsvq6JiGw99tYH1+4/yxpD29Gpe2xFlKKWUQ1x06kZEvIH3gb5AMrBeROYZY7aXWO1ZYIYxZqKItAIWANH228OA1kB9YImINDPGFJf3Czkfm83w1OwtLNlxhJcGtWZQB21SppTyLGUZ0XcFEowxe40xBcB0YNA56xjg1IR3MHDQfnsQMN0Yk2+M2Qck2LdXKYwx/HvBDmb/nsyj1zdj1FXRlbVrpZRyGmUJ+nAgqcT9ZPuykiYAI0UkGWs0P+4SnouIjBWRWBGJTUtLK2PpF/fBij188ss+Rl8dzcN9mpTbdpVSypWU18HY4cBUY0wE0B/4XETKvG1jzMfGmBhjTEytWrXKpaAv1h7gv4t28ZeO4Tw3oJU2KVNKeayynF6ZAkSWuB9hX1bSPUA/AGPMGhEJAMLK+NxyN3/zQf713VZ6t6jNa7e30yZlSimPVpZR93qgqYg0FBE/rIOr885ZJxHoAyAiLYEAIM2+3jAR8ReRhkBTYF15FV+an3en8eg3G4lpUIP37+ikTcqUUh7voiN6Y0yRiDwELAK8gU+NMdtE5EUg1hgzD3gcmCQij2IdmB1tjDHANhGZAWwHioAHK/KMm98Tj3H/53E0qV2NydqkTCmlABArj51HTEyMiY2NveTn7T6SzeAP1xBSxZeZ919F7Wrav0Yp5TlEJM4YE1PaY24zr1E9wJd2EcFWkzINeaWUOs1tet3UDQ7g83u6OboMpZRyOm4zoldKKVU6DXqllHJzGvRKKeXmNOiVUsrNadArpZSb06BXSik3p0GvlFJuToNeKaXcnNO1QBCRNODAFWwiDEgvp3Jcgae9XtDX7Cn0NV+aBsaYUvu8O13QXykRiT1fvwd35GmvF/Q1ewp9zeVHp26UUsrNadArpZSbc8eg/9jRBVQyT3u9oK/ZU+hrLiduN0evlFLqbO44oldKKVWCBr1SSrk5twl6EeknIrtEJEFExju6noomIp+KSKqIbHV0LZVFRCJFZLmIbBeRbSLyiKNrqmgiEiAi60Rkk/01v+DomiqDiHiLyAYRme/oWiqLiOwXkS0islFELv16qhfatjvM0YuIN7Ab6AskA+uB4caY7Q4trAKJyLVADjDNGNPG0fVUBhGpB9QzxvwuItWAOOAWN/87CxBkjMkREV/gF+ARY8xaB5dWoUTkMSAGqG6MGeDoeiqDiOwHYowx5f4lMXcZ0XcFEowxe40xBcB0YJCDa6pQxpiVwFFH11GZjDGHjDG/229nAzuAcMdWVbGMJcd+19f+4/qjswsQkQjgJmCyo2txF+4S9OFAUon7ybh5AHg6EYkGOgK/ObaSimefxtgIpAI/GWPc/TW/BTwJ2BxdSCUzwGIRiRORseW5YXcJeuVBRKQqMBv4uzHmuKPrqWjGmGJjTAcgAugqIm47VSciA4BUY0yco2txgGuMMZ2AG4EH7dOz5cJdgj4FiCxxP8K+TLkZ+zz1bOBLY8wcR9dTmYwxmcByoJ+ja6lAPYCB9vnq6UBvEfnCsSVVDmNMiv2/qcBcrCnpcuEuQb8eaCoiDUXEDxgGzHNwTaqc2Q9MfgLsMMa86eh6KoOI1BKREPvtQKwTDnY6tqqKY4x52hgTYYyJxvp3vMwYM9LBZVU4EQmyn2CAiAQBNwDldkadWwS9MaYIeAhYhHWAboYxZptjq6pYIvI1sAZoLiLJInKPo2uqBD2AUVijvI32n/6OLqqC1QOWi8hmrAHNT8YYjznl0IPUAX4RkU3AOuAHY8zC8tq4W5xeqZRS6vzcYkSvlFLq/DTolVLKzWnQK6WUm9OgV0opN6dBr5RSbk6DXnkMESkucVrmxvLscioi0Z7USVS5Fh9HF6BUJcq1txJQyqPoiF55PHsf8NfsvcDXiUgT+/JoEVkmIptFZKmIRNmX1xGRufYe8ZtE5Gr7prxFZJK9b/xi+zdZEZGH7T30N4vIdAe9TOXBNOiVJwk8Z+pmaInHsowxbYH3sLonArwLfGaMaQd8CbxjX/4O8LMxpj3QCTj1LeymwPvGmNZAJnCbffl4oKN9O/dX1ItT6nz0m7HKY4hIjjGmainL9wO9jTF77U3TDhtjQkUkHetCJ4X25YeMMWEikgZEGGPyS2wjGqs9QVP7/acAX2PMyyKyEOsiMd8C35boL69UpdARvVIWc57blyK/xO1izhwDuwl4H2v0v15E9NiYqlQa9EpZhpb47xr77dVYHRQBRgCr7LeXAg/A6YuCBJ9voyLiBUQaY5YDTwHBwB8+VShVkXRkoTxJoP1KTacsNMacOsWyhr1DZD4w3L5sHDBFRJ4A0oAx9uWPAB/bO4YWY4X+ofPs0xv4wv5mIMA79r7ySlUanaNXHq8iL8qslDPQqRullHJzOqJXSik3pyN6pZRycxr0Sinl5jTolVLKzWnQK6WUm9OgV0opN/f/IFz6w3EWvX8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65JB43g0cmVL"
      },
      "source": [
        "The `plot_loss_curves` function works great with models which have only been fit once, however, we want something to compare one series of running `fit()` with another (e.g. before and after fine-tuning)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-i6vsPXcc1o"
      },
      "source": [
        "# Let's create a function to compare training histories\n",
        "def compare_historys(original_history, new_history, initial_epochs=5):\n",
        "  \"\"\"\n",
        "  Compares two TensorFlow History objects.\n",
        "  \"\"\"\n",
        "  # Get original history measurements\n",
        "  acc = original_history.history[\"accuracy\"]\n",
        "  loss = original_history.history[\"loss\"]\n",
        "\n",
        "  val_acc = original_history.history[\"val_accuracy\"]\n",
        "  val_loss = original_history.history[\"val_loss\"]\n",
        "\n",
        "  # Combine original history metrics with new_history metrics\n",
        "  total_acc = acc + new_history.history[\"accuracy\"]\n",
        "  total_loss = loss + new_history.history[\"loss\"]\n",
        "\n",
        "  total_val_acc = val_acc + new_history.history[\"val_accuracy\"]\n",
        "  total_val_loss = val_loss + new_history.history[\"val_loss\"]\n",
        "\n",
        "  # Make plot for accuracy\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.subplot(2, 1, 1)\n",
        "  plt.plot(total_acc, label=\"Training Accuracy\")\n",
        "  plt.plot(total_val_acc, label=\"Val Accuracy\")\n",
        "  plt.plot([initial_epochs-1, initial_epochs-1], plt.ylim(), label=\"Start Fine Tuning\")\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.title(\"Training and Validation Accuracy\")\n",
        "\n",
        "  # Make plot for loss\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.subplot(2, 1, 2)\n",
        "  plt.plot(total_loss, label=\"Training Loss\")\n",
        "  plt.plot(total_val_loss, label=\"Val Loss\")\n",
        "  plt.plot([initial_epochs-1, initial_epochs-1], plt.ylim(), label=\"Start Fine Tuning\")\n",
        "  plt.legend(loc=\"upper right\")\n",
        "  plt.title(\"Training and Validation Loss\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "xFxwebgxd7b6",
        "outputId": "51825913-8b53-4ab6-e327-a5c7dd920b6c"
      },
      "source": [
        "compare_historys(history_10_percent_data_aug,\n",
        "                 history_fine_10_percent_data_aug,\n",
        "                 initial_epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gU1Y4BG4ShMQOqZ7K7kDDWCk14JpshkOFVezKLuOL7FL25RSQdywff9txgqkh1KuekQGGFF9DpJ+NyGAbYT5NhHo3EuLVSJBHI4GeDfjTgEdTLTTUQGMtVNdCRUdBWkurzjk94eFltRa9/axnLx/r0K5pApvN/tzU5rmd+b3dfl/4BFshO+Z0iPqeFb5RSdb5XZ+Aga9HtUvDWCnVe8ZAQzXUVkBdhf25vNW0qS3neGUpZSVFVFeW0nC8DKmrINBUs0CquYQavKUJ2t6auM7+qGhnu15+1sPb/+RnvzAIbp72ax2ivX32dNKvSmPaCe0m65yss+f7BkPUeAgaCe50WdQwpWGs1HDVjSBtea4tb3+ZusrWl7C0txkEm/EHArARAN7BeIbGIMHheIZF4hUWCX4h1sM31Hr2CXQIwzZh6+nrvodQRezBrr96VWv6L0INTsZA5VEo3AeF+08811ZAQAQERkFAlP05ss10lLXMAPbszC2rYdvhEjKLj+Mhwj+2ZDI61J/RA3XTBmOs6yiPF1nnCo8X2l8XOcyzT1eXnAhW09T5esXDamE1h6RviHU+MXoS+IXQ4B3MsTofjlR5crDCkz0lhm8rPKkkgCoCiIqKZnzcKJLHhDM9LoyJo4Px9RoCPW6VcrJuhbGILAIeAzyBp4wxD7d5fwzwHBBmX2a1MWa9k2tVQ5ExUJ7jELh77a/3W4HRzD/cCoDwsVawHP3SCpba8g5WLFYgdxjY7Ux7dm8IN2MMBwuq2JpZwrbDJWzLLCW3rAaAoATrnsG/eGN3q8/4e3u2BPPoUD9Gh/kT63Dzh5NuZ2iMdQ3n8WKHQG0nWB2nm+raL9jLz7rpQvN3jkwCP4dwbXkObTMdAj5BLYc4G5ts7M+v5Muccr7ILmPXgTIOFFTRZB8/LybUj+T4MM6ND2N6XCjTYkMJHubD4inVXWI6Gi+reQERT+Ab4FwgB9gGrDDG7HFYZg2w0xjzFxGZDKw3xiR0tt7U1FSTkZHRx/KV27DZoOxI61Zu4T7rzj31VSeWC4yG6IkQPaH1c2B0++e9mho6DqeOWoUddaLxC23dug6MhIAomvwjya4PZHeZNxmFnnx6FA7X+FOPN9HBvqQlRDA7IZzZiRE88uWdCPDbM//S+kb/pccpLy2gpjSfhsoCPGpKiKCCSCqIkAoipYIRnlWM8KgiXCoIbirHkw5arT5B3fjj4kT9+AT2+JyhMYYjxdV8kVPGF9nlfJFTxu68cmobrEuGwgK8mR4XRkpcKNPjwpgeH8qIYL8ebUOp4UZEthtjUtt7rzst4zTgoDHmkH1la4ElwB6HZQwQYn8dCuT1vlzl1poarVvhFe5rE7oHrF6tzYJHW0E745oTgRs1wQqQnvD0huBR1qM7bE3WjepbhXWRQwu0kKaqQmrzD8DxLfg1lOGJjQQgAbioeT1+YPMOQoKikOooOBIFhVFIzWGw2Rjxzs2MOF5MSvM2akpa3zjBocHY4BXMce8wKiSUYkazv3E8uU2B5DUEUmxCKCGYYhNCKSFIYBRRISH2FvWJVnVzqzs6uHe3ViyorOWL7HK+zCljV3YZX+aUU15jXUrk5+3B1JhQrp4zlulxoaTEhzEmIsC97pWs1CDXnTCOBRwvTssB5rRZ5gHgfRG5HQgEFjqlOjV4NTVYt8hre0636EDrw6UhcVbYJpzVurXrH+aauj08rZZjYFTLrJLj9WxrPuScX8LXeRU02QweApNHBXFWvA9zRtpIjmgknIqWAPdwPIRckWsdOg+yWduoq7FapVFJ1iUlHbViAyLx9vIhDOsczxiHUmvqm04eaMD++mBhFZsPFHY46MCJ+x87BLb9to1ensJXueVWize7jC9zysgrrwXA00MYPzKYC6eNYnpcGMlxYYwfGYTXUL0JhlKDhLM6cK0AnjXG/F5ETgf+ISJTjTGtboMjIjcDNwOMGTOmndWoQaexzrpZQNvQLT7Yuhdt2FgraE85xx64E60g8gvpeN0uklNazbbMErYeLmVbZgkHC6zD5D5eHqTEhfHDueOYnRDBzLHhhPT0nOe79pvKL3qmz3X6+3hySnQQp0QHtft+820f88qtofdyy04Mx5dXVsOu7DLe+froScPxORobGUBqQkRLi3dKTCj+PtrBSqmB1p0wzgXiHabj7PMcfR9YBGCM2SIifkAUUOC4kDFmDbAGrHPGvaxZ9YemBitoC/a1PsRccsihx61ARKIVtBMuONHKjRpvnZcchGw2w4GCKqvla2/9NrcCg329mJUQzqUzYklLjGBabKhbDbouIoQGeBMa4M2k0e3/0dM8UP1R+7B+uWW11DY0MTU2lOmxoYQH+gxw1Uqp9nQnjLcBSSKSiBXCy4Gr2iyTBSwAnhWRSYAfUOjMQpUTGWMN7J27A3K3W4+jX9jvUIR1W73IU2DERJhyyYnQjTx1wG+e3lP1jTa+ziu393IuIeNIacttFEcE+zI7MYIfJESQmhDOxFEh7jl0XQ94eAgjgv0YEWz1dFZKDU5dhrExplFEbgPew7ps6WljzG4ReRDIMMasA34MPCkiq7A6c600XXXTVgPneNGJ0G1+1JRa73n5wegUSP0+xM6EEZOt0PVyjxbT8bpGdmaVtVxmtDO7tKXHb2JUIOdNHsnshAjSEiO005FSatDq1jlj+zXD69vMu8/h9R7gTOeWpnqlvtpq5ToGb9kR6z3xsK7VnXgxxM6yHiMmdfv62sGguKqObZnWud6MzNadrSaNDmH57DGkJVotX73URinlLvQOXO7M1mSd283dDjkZ1mHngj0nzvGGxlut3dnfh9hUGJ0Mvu13BhqsjDF8nVvBB3vz2bAnnz1HrRuB+Hh5kBIfxi1zTyE1IZxZY8P1BhNKKbelYewujLGGP2tp8e6AvF3QcNx63y/UaulO+JH1HDMTgke6tuZeqm1oYsu3xXywN58P9+aTX1GHh8CsseH85PwJzEmMYFpcqN5WUSk1ZGgYD1Y1pfYOVjsgN8MK4OP2PnGePjBqOsy89sTh5ohxbj0yS3FVHR/tK2DD3nw+PlBEdX0TAT6ezB0fzcJJI5k/cQQR2vNXKTVEaRgPBg21cOyr1ud5S7498X7UBDj1XOuQc+wsGDnVbTpYdeZgQRUb7Ieft2eVYgyMCvHjspmxLJw0ktPGRbrVpUZKKdVbGsYDzWaD4gOtg/fY12CzLr8haBTEpVq3iYydBTEp1iHoIaCxycb2I6VWAO8t4HCRdYh9SkwId5yTxLmTRzIlJkR7PCulhh0N44Gy43n46hXrPG/zaEQ+wRA7A8647cTh5pAY19bpZFV1jWz+ppANe/L5aH8BZdUNeHsKp58SxQ1nJrBg0khiwgb3tctKKdXfNIwHwn8egY2/sq7hnb7sRPBGJrnvIOmdyCur4cO9+Xywt4DPvi2mvslGWIA350wYwcLJIzkrKUp7PiullAMN4/626WHY9BuYvhwu+fOADng/UIwx7M6r4IM9+WzYm8/uPKvlnxgVyHVnjGXhpJHMGhuugw0opVQHNIz7izGw8dew+XeQcjUs/t8hFcR1jdblRxv25vPh3gKOlte2XH50zwUTWTh5ZIcDHCillGpNw7g/GAMfPgj//QPM/B5c/NiQOBxdcryejfbLjzZ/U8hx++VHZydF8+PzRjJ/QjSRQb6uLlMppdyOhrGzGQMb7odPHoNZ18NFf3DrID5U2Hz5UQEZR0qwGRgZ4sslM2JZOHkkp+vlR0op1Wcaxs5kDLx/L2z5E8y+ES54xO2CuMlm2JFVyoY9+XywN59DhdblR5NHh3DbOUmcO2kkU2P18iOllHImDWNnMQbevQc+/wvM+SEsetit7oj1RXYZ//jsCB/uzafUfvnRaeMiWXmGdflRrF5+pJRS/UbD2BmMgfU/gW1Pwmm3wvkPuUUQ22yGTd8U8Lf/HOLzwyUE+3qxcPJIFk4aydnj9fIjpZQaKBrGfWWzwfofQ8bTcMbtcO4vB30Q1zU28cauPJ7cfIgDBVXEhPpx70WTWJ42hiBf/SehlFIDTX/z9oXNBm/9j3V3re+sggX3D+ogLq9p4IXPj/DsJ5kUVNYxaXQIf7wyhYumj8ZbrwFWSimX0TDuLVsTrLsDdv0TzroLzrl30AZxblkNT//3MGu3ZnG8vomzkqL4/bJkvnNqlHbEUkqpQUDDuDdsTfDGrfBFOsxdDfNWD8og3pNXwZrN3/LWl0cxwHenj+ams8cxJWZoDDyhlFJDhYZxTzU1wr9vga9ehvk/h7l3u7qiVowx/PdgEWs2H+LjA0UE+nhy3RkJ3PCdRO0RrZRSg5SGcU80NcLrN8PXr8E5v4Cz73J1RS0ammy8/eVR1mw+xJ6jFUQH+3L3oglcnTaW0ADtFa2UUoNZt8JYRBYBjwGewFPGmIfbvP//gPn2yQBghDEmzJmFulxTA7x2I+z5Nyz8v/CdO11dEWANUbh2axZP//cweeW1nDoiiN9dPp0lM2Lw9dI7YymllDvoMoxFxBN4AjgXyAG2icg6Y8ye5mWMMasclr8dmNEPtbpOYz28dgPsfRPO+5V1CZOL5VfU8swnmbzw+REqaxtJS4zgl5dMZf6EEXh4DL7z10oppTrWnZZxGnDQGHMIQETWAkuAPR0svwK43znlDQKN9fDKStj/tnVXrdNucWk5B/IrWbP5EP/elUuTzbBo6ihuPvsUUuKH1oEIpZQaTroTxrFAtsN0DjCnvQVFZCyQCHzU99IGgcY6ePl78M271n2m59zskjKMMXx+uIQ1mw/x0b4C/Lw9WD57DDeelcjYyECX1KSUUsp5nN2BaznwqjGmqb03ReRm4GaAMWPGOHnTTtZQCy9fCwfeh4t+bw38MMCabIZ3vz7Gms3f8kVOORGBPty5MInvnZ5ARKDPgNejlFKqf3QnjHOBeIfpOPu89iwHbu1oRcaYNcAagNTUVNPNGgdeQw2svRq+/RAu/iOkXj+gm6+pb+KV7dk89fFhskqqSYgM4FeXTOXymXH4+2inLKWUGmq6E8bbgCQRScQK4eXAVW0XEpGJQDiwxakVDrT6alh7FRzaBIv/BDOvHbBNF1fV8dyWI/xjSyal1Q3MGBPGzy6cyLmTR+GpnbKUUmrI6jKMjTGNInIb8B7WpU1PG2N2i8iDQIYxZp190eXAWmPM4G3xdqX+OKQvh8MfwyV/hpST/uboF4eLjvPUx4d4dXsOdY02Fk4ayQ/mjiN1bLjerlIppYaBbp0zNsasB9a3mXdfm+kHnFeWC9RVwYtXQtancOnfIPnKft/k9iOlrNn8Le/vycfbw4PLZsZy41njOHVEUL9vWyml1OChd+ACqKuEF5ZB9mdw2ZMwbWm/bcpmM2zYm8+azYfIOFJKiJ8X/2feKVx3RgIjgv36bbtKKaUGLw3j2gp4YSnkZMDlf4epl/XPZhqaeH1nLk9+fIhDhceJDfPnvosnc+XseAJ1DGGllBrWhncK1JbDPy+HvJ1wxTMweUm/ber6Z7ax5VAxU2JCeGx5ChdNG42XjiGslFKK4RzGNWXwj0vh2JdwxbMw6bv9tqndeeVsOVTMj84dz+3nnKqdspRSSrUyPMO4usQK4vzdsOwfMPHCft3c2q3Z+Hh58L3Tx2oQK6WUOsnwC+PqEnh+CRTug+UvwPjz+3dz9Y38e2cuF00bTViA3jVLKaXUyYZXGB8vsoK46AAsT4ekhf2+ybe+PEplXSMr0gb57T+VUkq5zPAJ46pCeH4xlByCFelw6oIB2Wz61ixOiQ5kdkL4gGxPKaWU+xke3XmrCuC5i6HkMFz10oAF8d6jFezMKmNF2hg9V6yUUqpDQ79lXHkMnvsulOfA1S9D4tkDtum1W7Pw8fTg8plxA7ZNpZRS7mdoh3FFnhXEFUfh6lch4cwB23RNfRP/2pnLBdNGEa7DHSqllOrE0A3j8lzr0HRVAVz7Lxhz2oBu/u2vjlJZqx23lFJKdW1ohnFZthXEx4vh2tchPm3AS0jfmsW4qEDmJEYM+LaVUkq5l6HXgav0CDx7oXU98ff+7ZIg/ia/ku1HSlmeFq8dt5RSSnVpaLWMSw5b54jrKqwgjp3lkjLSt2bh7SnacUsppVS3DJ0wLjkEz34X6qvge+sgJgySLfEAAB0DSURBVMUlZdQ2NPGvHbmcP2UUkUG+LqlBKaWUexkaYVz8LTx7MTTWwnVvwujpLivlna+PUl7TwFXacUsppVQ3DY0wztsJtkYriEdNdWkp6Z9nkxAZwGnjIl1ah1JKKfcxNMJ42lJIOg/8QlxaxsGCSrZmlrD6gol4eGjHLaWUUt0zdHpTuziIAdK3ZuPtKSydpR23lFJKdV+3wlhEFonIfhE5KCKrO1hmmYjsEZHdIvKic8sc/GobmnhtRw7nTR5FlHbcUkop1QNdHqYWEU/gCeBcIAfYJiLrjDF7HJZJAu4BzjTGlIrIiP4qeLB6b/cxyqob9I5bSimleqw7LeM04KAx5pAxph5YCyxps8xNwBPGmFIAY0yBc8sc/F78PIv4CH/OOEU7bimllOqZ7oRxLJDtMJ1jn+doPDBeRD4Rkc9EZJGzCnQHhwqr+PxwCctnj9GOW0oppXrMWb2pvYAkYB4QB2wWkWnGmDLHhUTkZuBmgDFjhs7h3LXbsvHyEK5I1Y5bSimleq47LeNcIN5hOs4+z1EOsM4Y02CMOQx8gxXOrRhj1hhjUo0xqdHR0b2teVCpa2zi1e05LJw0khHBfq4uRymllBvqThhvA5JEJFFEfIDlwLo2y/wbq1WMiERhHbY+5MQ6B633d+dTcryeFXOGTktfKaXUwOoyjI0xjcBtwHvAXuBlY8xuEXlQRBbbF3sPKBaRPcBG4CfGmOL+KnowSd+aRWyYP2edGuXqUpRSSrmpbp0zNsasB9a3mXefw2sD/Mj+GDYOFx3n02+Lueu88dpxSymlVK8NnTtwucDabVl4eghXpMZ3vbBSSinVAQ3jXqpvtPFqRg4LJo5gZIh23FJKKdV7Gsa99MGefIq145ZSSikn0DDupeaOW2cnDY1LtJRSSrmOhnEvZBVX89+DRSxLjcdTO24ppZTqIw3jXli7LQsPgWWz9Y5bSiml+k7DuIcammy8nJHDORNHMDrU39XlKKWUGgI0jHvow735FFXV6VCJSimlnEbDuIde3JrN6FA/5o7XjltKKaWcQ8O4B7JLqvn4QCHLUuPx8tRdp5RSyjk0UXrgpW3ZCLBstt5xSymllPNoGHeT1XErm3kTRhAbph23lFJKOY+GcTd9tK+AgkrtuKWUUsr5NIy7ae3WLEaG+DJ/gnbcUkop5Vwaxt2QW1bDpm+045ZSSqn+ocnSDS9tywZgmQ6VqJRSqh9oGHehscnGy9uyOTspmviIAFeXo5RSagjSMO7Cpv2FHKuo1Y5bSiml+o2GcRfSt2YRHezLgkkjXF2KUkqpIUrDuBN5ZTVs3F/AstQ4vLXjllJKqX7SrYQRkUUisl9EDorI6nbeXykihSKyy/640fmlDryXM7KxGVg+Ww9RK6WU6j9eXS0gIp7AE8C5QA6wTUTWGWP2tFn0JWPMbf1Qo0s02Qwvb8vmrKQo7billFKqX3WnZZwGHDTGHDLG1ANrgSX9W5brbf6mkLzyWq7SjltKKaX6WXfCOBbIdpjOsc9r63IR+VJEXhURt78g98WtWUQF+bBg0khXl6KUUmqIc1avpDeBBGPMdOAD4Ln2FhKRm0UkQ0QyCgsLnbRp5ztWXstH+wpYOiseHy/tuKWUUqp/dSdpcgHHlm6cfV4LY0yxMabOPvkUMKu9FRlj1hhjUo0xqdHRg/cez69kZNNkMyzXoRKVUkoNgO6E8TYgSUQSRcQHWA6sc1xAREY7TC4G9jqvxIHVZDOs3ZbNmadGkhAV6OpylFJKDQNd9qY2xjSKyG3Ae4An8LQxZreIPAhkGGPWAXeIyGKgESgBVvZjzf3q4wOF5JbVcM+FE11dilJKqWGiyzAGMMasB9a3mXefw+t7gHucW5prpG/NIjLQh/Mmj3J1KUoppYYJ7Z3koKCilg17C1g6K047bimllBowmjgOXtmeQ5PNcKV23FJKKTWANIztbDbD2m1ZnD4uknHRQa4uRyml1DCiYWz3ybdFZJfUsGKO3nFLKaXUwNIwtkvfmkV4gDfnT9E7bimllBpYGsZAYWUd7+/O5/KZcfh6ebq6HKWUUsOMhjHw6vYcGm2G5ToohFJKKRcY9mHc3HErLTGCU0doxy2llFIDb9iH8ZZDxRwprtahEpVSSrnMsA/jF7dmEervzaKpesctpZRSrjGsw7ioqo73dx/j8plx+Hlrxy2llFKu0a17Uw9Vr23PoaHJsCJN77illOpfDQ0N5OTkUFtb6+pSVD/z8/MjLi4Ob2/vbn9m2IaxMdZQibMTwkkaGezqcpRSQ1xOTg7BwcEkJCQgIq4uR/UTYwzFxcXk5OSQmJjY7c8N28PUnx0q4XDRcVZoxy2l1ACora0lMjJSg3iIExEiIyN7fARk2IZx+tYsQvy8uHDaaFeXopQaJjSIh4fe/JyHZRiXHK/n3a+PcZl23FJKDRPFxcWkpKSQkpLCqFGjiI2NbZmur6/v9LMZGRnccccdXW7jjDPOcFa5ANx5553ExsZis9mcut7BaFieM/7Xjhzqm2ws145bSqlhIjIykl27dgHwwAMPEBQUxF133dXyfmNjI15e7UdCamoqqampXW7j008/dU6xgM1m4/XXXyc+Pp7//Oc/zJ8/32nrdtTZ9x5Iw65lbIzhxa1ZzBwTxsRRIa4uRymlXGblypX88Ic/ZM6cOdx9991s3bqV008/nRkzZnDGGWewf/9+ADZt2sTFF18MWEF+ww03MG/ePMaNG8fjjz/esr6goKCW5efNm8fSpUuZOHEiV199NcYYANavX8/EiROZNWsWd9xxR8t629q0aRNTpkzhlltuIT09vWV+fn4+l156KcnJySQnJ7f8AfD8888zffp0kpOTufbaa1u+36uvvtpufWeddRaLFy9m8uTJAFxyySXMmjWLKVOmsGbNmpbPvPvuu8ycOZPk5GQWLFiAzWYjKSmJwsJCwPqj4dRTT22Z7i3X/zkwwLYeLuFQ4XEeWTrd1aUopYap//vmbvbkVTh1nZNjQrj/u1N6/LmcnBw+/fRTPD09qaio4OOPP8bLy4sNGzbws5/9jNdee+2kz+zbt4+NGzdSWVnJhAkTuOWWW066jGfnzp3s3r2bmJgYzjzzTD755BNSU1P5wQ9+wObNm0lMTGTFihUd1pWens6KFStYsmQJP/vZz2hoaMDb25s77riDuXPn8vrrr9PU1ERVVRW7d+/mV7/6FZ9++ilRUVGUlJR0+b137NjB119/3dLj+emnnyYiIoKamhpmz57N5Zdfjs1m46abbmqpt6SkBA8PD6655hpeeOEF7rzzTjZs2EBycjLR0dE93POtDbuWcfrWLIL9vLh4eoyrS1FKKZe74oor8PS0+s6Ul5dzxRVXMHXqVFatWsXu3bvb/cxFF12Er68vUVFRjBgxgvz8/JOWSUtLIy4uDg8PD1JSUsjMzGTfvn2MGzeuJQA7CuP6+nrWr1/PJZdcQkhICHPmzOG9994D4KOPPuKWW24BwNPTk9DQUD766COuuOIKoqKiAIiIiOjye6elpbW69Ojxxx8nOTmZ0047jezsbA4cOMBnn33G2Wef3bJc83pvuOEGnn/+ecAK8euvv77L7XWlWy1jEVkEPAZ4Ak8ZYx7uYLnLgVeB2caYjD5X52Slx+tZ//Uxls+Ox99HO24ppVyjNy3Y/hIYGNjy+he/+AXz58/n9ddfJzMzk3nz5rX7GV9f35bXnp6eNDY29mqZjrz33nuUlZUxbdo0AKqrq/H39+/wkHZHvLy8Wjp/2Wy2Vh3VHL/3pk2b2LBhA1u2bCEgIIB58+Z1emlSfHw8I0eO5KOPPmLr1q288MILPaqrPV22jEXEE3gCuACYDKwQkcntLBcM/A/weZ+r6if/2plLfaON5bP12mKllGqrvLyc2NhYAJ599lmnr3/ChAkcOnSIzMxMAF566aV2l0tPT+epp54iMzOTzMxMDh8+zAcffEB1dTULFizgL3/5CwBNTU2Ul5dzzjnn8Morr1BcXAzQcpg6ISGB7du3A7Bu3ToaGhra3V55eTnh4eEEBASwb98+PvvsMwBOO+00Nm/ezOHDh1utF+DGG2/kmmuuaXVkoS+6c5g6DThojDlkjKkH1gJL2lnul8BvgUF5rzdjDGu3ZpESH8bkGO24pZRSbd19993cc889zJgxo0ct2e7y9/fnz3/+M4sWLWLWrFkEBwcTGhraapnq6mreffddLrroopZ5gYGBfOc73+HNN9/kscceY+PGjUybNo1Zs2axZ88epkyZws9//nPmzp1LcnIyP/rRjwC46aab+M9//kNycjJbtmxp1Rp2tGjRIhobG5k0aRKrV6/mtNNOAyA6Opo1a9Zw2WWXkZyczJVXXtnymcWLF1NVVeWUQ9QA0tzDrcMFRJYCi4wxN9qnrwXmGGNuc1hmJvBzY8zlIrIJuKurw9SpqakmI2PgjmRnZJaw9K9b+N3l01k2Wy9pUv3j+net/5jPLHrGxZWowWbv3r1MmjTJ1WW4XFVVFUFBQRhjuPXWW0lKSmLVqlWuLqvHMjIyWLVqFR9//HG777f38xaR7caYdq8R63MHLhHxAP4A/Lgby94sIhkiktHXbuA99eLWLIJ8vbg4We+4pZRSrvLkk0+SkpLClClTKC8v5wc/+IGrS+qxhx9+mMsvv5zf/OY3Tltndzpw5QKOTck4+7xmwcBUYJP9FmCjgHUisrht69gYswZYA1bLuA9190h5dQNvf3mUpbPiCPAZdldzKaXUoLFq1Sq3bAk7Wr16NatXr3bqOrvTMt4GJIlIooj4AMuBdc1vGmPKjTFRxpgEY0wC8BlwUhC70us7c6hrtOmgEEoppQalLsPYGNMI3Aa8B+wFXjbG7BaRB0VkcX8X2FfGGNK3ZjM9LpSpsaFdf0AppZQaYN06ZmuMWQ+sbzPvvg6Wndf3spxnR1YZ+/Mr+c1l01xdilJKKdWuIX8HrvStWQT6ePLdZL3jllJKqcFpSIdxeU0Db32Zx+KUWIJ8teOWUmr4mj9/fsstJZv98Y9/bLm1ZHvmzZtHR5egFhUV4e3tzV//+len1jlcDekwXrcrl9oGG1dpxy2l1DC3YsUK1q5d22re2rVrOx2soTOvvPIKp512WqsRlfpDf9x8ZDAasmFsjOGFz7OYGhvCtDjtuKWUGt6WLl3K22+/3XJ/5szMTPLy8jjrrLO45ZZbSE1NZcqUKdx///3dWl96ejq///3vyc3NJScnp2V+e0MZtjfsYWZmJlOnTm353KOPPsoDDzwAWC3yO++8k9TUVB577DHefPNN5syZw4wZM1i4cGHLwBTNd8CaNm0a06dP57XXXuPpp5/mzjvvbFnvk08+6RaXUg3ZY7df5JSz71glD106teuFlVJqIL2zGo595dx1jpoGF7Q7hg9gjTiUlpbGO++8w5IlS1i7di3Lli1DRHjooYeIiIigqamJBQsW8OWXXzJ9esfDzGZnZ3P06FHS0tJYtmwZL730Ej/+8Y87HMqwvWEPS0tLO/069fX1LYfIS0tL+eyzzxARnnrqKX73u9/x+9//nl/+8peEhoby1VdftSzn7e3NQw89xCOPPIK3tzfPPPMMf/vb33q6NwfckG0Zp3+ehb+3J4u145ZSSgGtD1U7HqJ++eWXmTlzJjNmzGD37t3s2bOn0/W89NJLLFu2DIDly5e3HKruaCjD9oY97IrjfaBzcnI4//zzmTZtGo888kjL0I4bNmzg1ltvbVkuPDycoKAgzjnnHN566y327dtHQ0NDy+hPg9mQbBlX1jaw7os8FifHEOzn3fUHlFJqIHXSgu1PS5YsYdWqVezYsYPq6mpmzZrF4cOHefTRR9m2bRvh4eGsXLmy0+EDwTpEfezYsZahA/Py8jhw4ECPanEc3hA4aZuOgzrcfvvt/OhHP2Lx4sVs2rSp5XB2R2688UZ+/etfM3HiRKcN5NDfhmTL+I1dedQ0NLFijnbcUkqpZkFBQcyfP58bbrihpVVcUVFBYGAgoaGh5Ofn884773S6jm+++Yaqqipyc3Nbhji85557SE9P73Aow/aGPRw5ciQFBQUUFxdTV1fHW2+91eE2HYd2fO6551rmn3vuuTzxxBMt082HvufMmUN2djYvvvhirzuoDbQhF8bGGF78PItJo0NI1o5bSinVyooVK/jiiy9aQio5OZkZM2YwceJErrrqKs4888xOP5+ens6ll17aat7ll19Oenp6h0MZtjfsobe3N/fddx9paWmce+65TJw4scNtPvDAA1xxxRXMmjWr5RA4wL333ktpaSlTp04lOTmZjRs3try3bNkyzjzzTMLDw3u8j1yhyyEU+0t/DaH4ZU4Zi//0Cb9cMoVrT09w+vqV6owOoag6okMoDqyLL76YVatWsWDBApdsf8CHUBxs0rdm4eftwZIZsa4uRSml1AArKytj/Pjx+Pv7uyyIe2NIdeCqqmtk3a48vjs9hhDtuKWUUsNOWFgY33zzjavL6LEh1TJ+84s8jtdrxy2llFLuZUiFcfrWLCaOCmZGfJirS1FKKaW6bciE8de55XyZU86KtDGIiKvLUUoppbptyIRx+tYsfL08uCRFO24ppZRyL0MijI/XNfLGrjwumj6a0ADtuKWUUu156KGHmDJlCtOnTyclJYXPP/8csIZSrK6u7vH6nn32WfLy8tp9b+XKlSQmJpKSkkJKSgqPP/449913Hxs2bOjTd/jqq69a1hkREdGyjYULF/Z4XRdeeCFlZWV9qsdZhkRv6re+zKOqrlGHSlRKqQ5s2bKFt956ix07duDr60tRUVHLCE5//OMfueaaawgICOj2+pqamnj22WeZOnUqMTHtjwHwyCOPsHTpUqfU32zatGns2rULsAL/4osv7vU21q9f78zS+mRItIy/kxTNvRdNYtZY97jTilJKDbSjR48SFRWFr68vAFFRUcTExPD444+Tl5fH/PnzmT9/PkCHQyomJCTw05/+lJkzZ5Kenk5GRgZXX301KSkp1NTUdFnDypUrefXVV1vWdf/99zNz5kymTZvGvn37ADh+/Dg33HADaWlpzJgxgzfeeKNb32/evHktozwVFRWRkJAAWK33yy67jEWLFpGUlMTdd9/d6vsUFRWRmZnJpEmTuOmmm5gyZQrnnXdey/fZtm1by5GEn/zkJ62GfXSmIdEyjg3z58azxrm6DKWU6pbfbv0t+0r2OXWdEyMm8tO0n3b4/nnnnceDDz7I+PHjWbhwIVdeeSVz587ljjvu4A9/+AMbN25sudVkZ0MqRkZGsmPHDgCeeuopHn30UVJT272pFD/5yU/41a9+BcA//vGPk96Piopix44d/PnPf+bRRx/lqaee4qGHHuKcc87h6aefpqysjLS0NBYuXNhq4Iie2rVrFzt37sTX15cJEyZw++23Ex8f32qZAwcOkJ6ezpNPPsmyZct47bXXuOaaa7j++ut58sknOf3001m9enWva+hKt1rGIrJIRPaLyEEROakaEfmhiHwlIrtE5L8iMtn5pSqllOqtoKAgtm/fzpo1a4iOjubKK6/k2WefbXfZzoZUdBzasCuPPPIIu3btYteuXe0OY3jZZZcBMGvWLDIzMwF4//33efjhh0lJSWHevHnU1taSlZXV/S/ajgULFhAaGoqfnx+TJ0/myJEjJy3TfO7ZsZ6ysjIqKys5/fTTAbjqqqv6VEdnumwZi4gn8ARwLpADbBORdcYYxwEvXzTG/NW+/GLgD8CifqhXKaXcXmct2P7k6enJvHnzmDdvHtOmTeO5555j5cqVrZbpakjFvrRQ22o+ZO7p6UljYyNgDfbz2muvMWHChB6ty3FIxrbDMTZvp+22OlumO4fdnak7LeM04KAx5pAxph5YCyxxXMAYU+EwGQi4ZvQJpZRS7dq/f3+rMYd37drF2LFjAQgODqayshLo2ZCKjp9zlvPPP5///d//pXkQo507d3brcwkJCWzfvh2g5bx0X4WFhREcHNzS63zt2rVOWW97unPOOBbIdpjOAea0XUhEbgV+BPgA5zilOqWUUk5RVVXF7bffTllZGV5eXpx66qmsWbMGgJtvvplFixYRExPDxo0bW4ZUjI+P73RIxZUrV/LDH/4Qf39/tmzZgr+/f5/r/MUvfsGdd97J9OnTsdlsJCYmdjrWcbO77rqLZcuWsWbNGi666KI+19Hs73//OzfddBMeHh7MnTuX0ND+GZq3yyEURWQpsMgYc6N9+lpgjjHmtg6Wvwo43xhzXTvv3QzcDDBmzJhZ7R23V8qd6RCKqiM6hKJ7qqqqIigoCICHH36Yo0eP8thjj3X5uf4YQjEXcOx2Fmef15G1wCXtvWGMWWOMSTXGpEZHR3dj00oppZTrvP3226SkpDB16lQ+/vhj7r333n7ZTncOU28DkkQkESuElwOtupSJSJIxpvlkxEXAAZQahiZGTHR1CUopJ7ryyit71IO8t7oMY2NMo4jcBrwHeAJPG2N2i8iDQIYxZh1wm4gsBBqAUuCkQ9RKDQeu6iWrlHJv3brphzFmPbC+zbz7HF7/j5PrUkqpIccYo6PKDQNd9cVqz5C4HaZSSg12fn5+FBcX9+oXtXIfxhiKi4vx8/Pr0eeGxO0wlVJqsIuLiyMnJ4fCwkJXl6L6mZ+fH3FxcT36jIaxUkoNAG9vbxITE11dhhqk9DC1Ukop5WIaxkoppZSLaRgrpZRSLtbl7TD7bcMihYAz74cZBRQ5cX3Dle7HvtN92He6D/tO92HfOXsfjjXGtHv7SZeFsbOJSEZH9/xU3af7se90H/ad7sO+033YdwO5D/UwtVJKKeViGsZKKaWUiw2lMF7j6gKGCN2Pfaf7sO90H/ad7sO+G7B9OGTOGSullFLuaii1jJVSSim3NCTCWEQWich+ETkoIqtdXY+7EZF4EdkoIntEZLeI6ChcvSQiniKyU0TecnUt7khEwkTkVRHZJyJ7ReR0V9fkjkRklf3/8tciki4iPRu1YBgSkadFpEBEvnaYFyEiH4jIAftzeH9t3+3DWEQ8gSeAC4DJwAoRmezaqtxOI/BjY8xk4DTgVt2HvfY/wF5XF+HGHgPeNcZMBJLRfdljIhIL3AGkGmOmYo1Dv9y1VbmFZ4FFbeatBj40xiQBH9qn+4XbhzGQBhw0xhwyxtQDa4ElLq7JrRhjjhpjdthfV2L9Aox1bVXuR0TigIuAp1xdizsSkVDgbODvAMaYemNMmWurcltegL+IeAEBQJ6L6xn0jDGbgZI2s5cAz9lfPwdc0l/bHwphHAtkO0znoEHSayKSAMwAPndtJW7pj8DdgM3VhbipRKAQeMZ+qP8pEQl0dVHuxhiTCzwKZAFHgXJjzPuurcptjTTGHLW/PgaM7K8NDYUwVk4iIkHAa8CdxpgKV9fjTkTkYqDAGLPd1bW4MS9gJvAXY8wM4Dj9eFhwqLKf11yC9cdNDBAoIte4tir3Z6xLj/rt8qOhEMa5QLzDdJx9nuoBEfHGCuIXjDH/cnU9buhMYLGIZGKdKjlHRP7p2pLcTg6QY4xpPirzKlY4q55ZCBw2xhQaYxqAfwFnuLgmd5UvIqMB7M8F/bWhoRDG24AkEUkUER+sjgrrXFyTWxERwTpPt9cY8wdX1+OOjDH3GGPijDEJWP8GPzLGaGukB4wxx4BsEZlgn7UA2OPCktxVFnCaiATY/28vQDvC9dY64Dr76+uAN/prQ179teKBYoxpFJHbgPeweg0+bYzZ7eKy3M2ZwLXAVyKyyz7vZ8aY9S6sSQ1PtwMv2P+wPgRc7+J63I4x5nMReRXYgXWlxE70blxdEpF0YB4QJSI5wP3Aw8DLIvJ9rFEGl/Xb9vUOXEoppZRrDYXD1EoppZRb0zBWSimlXEzDWCmllHIxDWOllFLKxTSMlVJKKRfTMFZKKaVcTMNYKaWUcjENY6WUUsrF/j8C+cOtevJxnwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FR7os068eA6h"
      },
      "source": [
        "## Model 4: Fine-tuning and existing model on all of the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbyNDcJAgZxg",
        "outputId": "cf5333c0-6179-4c8e-9580-0ba207498bbf"
      },
      "source": [
        "# Download and unzip 10 classes of Food101 data with all images\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
        "unzip_data(\"10_food_classes_all_data.zip\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-18 05:00:56--  https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.5.240, 172.217.13.80, 172.217.12.240, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.5.240|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 519183241 (495M) [application/zip]\n",
            "Saving to: ‘10_food_classes_all_data.zip’\n",
            "\n",
            "10_food_classes_all 100%[===================>] 495.13M   225MB/s    in 2.2s    \n",
            "\n",
            "2021-02-18 05:00:58 (225 MB/s) - ‘10_food_classes_all_data.zip’ saved [519183241/519183241]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EgyH6T8sgy9s"
      },
      "source": [
        "# Setup training and test dir\n",
        "train_dir_all_data = \"10_food_classes_all_data/train\"\n",
        "test_dir = \"10_food_classes_all_data/test\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COuUoMt-g_kC",
        "outputId": "2a87033b-17a8-42a5-aeda-9395ad497d36"
      },
      "source": [
        "# How many images are we working with now?\n",
        "walk_through_dir(\"10_food_classes_all_data\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 2 directories and 0 images in '10_food_classes_all_data'.\n",
            "There are 10 directories and 0 images in '10_food_classes_all_data/train'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/chicken_curry'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/hamburger'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/sushi'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/grilled_salmon'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/ice_cream'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/fried_rice'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/ramen'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/pizza'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/steak'.\n",
            "There are 0 directories and 750 images in '10_food_classes_all_data/train/chicken_wings'.\n",
            "There are 10 directories and 0 images in '10_food_classes_all_data/test'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/chicken_curry'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/hamburger'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/sushi'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/grilled_salmon'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/ice_cream'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/fried_rice'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/ramen'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/pizza'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/steak'.\n",
            "There are 0 directories and 250 images in '10_food_classes_all_data/test/chicken_wings'.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQ1ETOMPhD8W",
        "outputId": "ffed0b5a-ba9b-49df-e7cf-2d3dee316061"
      },
      "source": [
        "# Setup data inputs\n",
        "import tensorflow as tf\n",
        "IMG_SIZE = (224, 224)\n",
        "train_data_10_classes_full = tf.keras.preprocessing.image_dataset_from_directory(train_dir_all_data,\n",
        "                                                                                 label_mode=\"categorical\",\n",
        "                                                                                 image_size=IMG_SIZE)\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(test_dir,\n",
        "                                                                label_mode=\"categorical\",\n",
        "                                                                image_size=IMG_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 7500 files belonging to 10 classes.\n",
            "Found 2500 files belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFslYqR4iQMZ"
      },
      "source": [
        "The test dataset we've loaded in is the same as what we've been using for previous experiments (all experiments have used the same test dataset).\n",
        "\n",
        "Let's verify this..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuXTId5chmUS",
        "outputId": "a5a32291-116f-4438-96ab-62b875fbffeb"
      },
      "source": [
        "# Evaluate model 2 (this is the fine-tuned on 10 percent of data version)\n",
        "model_2.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 118ms/step - loss: 0.4812 - accuracy: 0.8416\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4812224209308624, 0.8416000008583069]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4TTG0gGigE-",
        "outputId": "27019c18-bc5c-4f9a-d07b-fdb0fdf1573e"
      },
      "source": [
        "results_fine_tune_10_percent"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.48122256994247437, 0.8416000008583069]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNGdjx0eiyYO"
      },
      "source": [
        "To train a fine-tuning model (model_4) we need to revert model_2 back to its feature extraction weights."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oQNEWXvikW5",
        "outputId": "d1864e3a-261e-4a72-99d7-b932fc2b6d99"
      },
      "source": [
        "# Load weights from checkpoint, that way we can fine-tune from \n",
        "# the same stage the 10 percent data model was fine-tuned from\n",
        "model_2.load_weights(checkpoint_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7efbf73d32b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZf-IlByjQwe",
        "outputId": "426841d9-8f13-4947-af74-1bb10b06be89"
      },
      "source": [
        "# Let's evaluate model_2 now\n",
        "model_2.evaluate(test_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 119ms/step - loss: 0.6907 - accuracy: 0.8128\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6906847953796387, 0.8127999901771545]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKSY4YVajbgC",
        "outputId": "8a125244-240e-440b-f975-a5ba0825f9d0"
      },
      "source": [
        "# Check to see if our model_2 has been reverted back to feature extraction results\n",
        "results_10_percent_data_aug "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.690684974193573, 0.8127999901771545]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0_xIDjEjjH7"
      },
      "source": [
        "Alright, the previous steps might seem quite confusing but all we've done is:\n",
        "\n",
        "1. Trained a feature extraction transfer learning model for 5 epochs on 10% of the data with data augmentation (model_2) and we saved the model's weights using `ModelCheckpoint` callback.\n",
        "2. Fine-tuned the same model on the same 10% of the data for a further 5 epochs with the top 10 layers of the base model unfrozen (model_3).\n",
        "3. Saved the results and training logs each time.\n",
        "4. Reloaded the model from step 1 to do the same steps as step 2 except this time we're going to use all of the data (model_4)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wbIN84FkN8G",
        "outputId": "866c1384-a303-424c-d836-8e8e21f94908"
      },
      "source": [
        "# Check which layers are tunable in the whole model\n",
        "for layer_number, layer in enumerate(model_2.layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_layer True\n",
            "1 data_augmentation True\n",
            "2 efficientnetb0 True\n",
            "3 global_average_pooling_2D True\n",
            "4 output_layer True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rhJLZANbkhgR",
        "outputId": "f6247ae9-2853-4bd7-ed3d-ebe43ef1e1f1"
      },
      "source": [
        "# Let's drill into our base_model (efficientnetb0) and see what layers are trainable\n",
        "for layer_number, layer in enumerate(model_2.layers[2].layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_3 False\n",
            "1 rescaling_2 False\n",
            "2 normalization_2 False\n",
            "3 stem_conv_pad False\n",
            "4 stem_conv False\n",
            "5 stem_bn False\n",
            "6 stem_activation False\n",
            "7 block1a_dwconv False\n",
            "8 block1a_bn False\n",
            "9 block1a_activation False\n",
            "10 block1a_se_squeeze False\n",
            "11 block1a_se_reshape False\n",
            "12 block1a_se_reduce False\n",
            "13 block1a_se_expand False\n",
            "14 block1a_se_excite False\n",
            "15 block1a_project_conv False\n",
            "16 block1a_project_bn False\n",
            "17 block2a_expand_conv False\n",
            "18 block2a_expand_bn False\n",
            "19 block2a_expand_activation False\n",
            "20 block2a_dwconv_pad False\n",
            "21 block2a_dwconv False\n",
            "22 block2a_bn False\n",
            "23 block2a_activation False\n",
            "24 block2a_se_squeeze False\n",
            "25 block2a_se_reshape False\n",
            "26 block2a_se_reduce False\n",
            "27 block2a_se_expand False\n",
            "28 block2a_se_excite False\n",
            "29 block2a_project_conv False\n",
            "30 block2a_project_bn False\n",
            "31 block2b_expand_conv False\n",
            "32 block2b_expand_bn False\n",
            "33 block2b_expand_activation False\n",
            "34 block2b_dwconv False\n",
            "35 block2b_bn False\n",
            "36 block2b_activation False\n",
            "37 block2b_se_squeeze False\n",
            "38 block2b_se_reshape False\n",
            "39 block2b_se_reduce False\n",
            "40 block2b_se_expand False\n",
            "41 block2b_se_excite False\n",
            "42 block2b_project_conv False\n",
            "43 block2b_project_bn False\n",
            "44 block2b_drop False\n",
            "45 block2b_add False\n",
            "46 block3a_expand_conv False\n",
            "47 block3a_expand_bn False\n",
            "48 block3a_expand_activation False\n",
            "49 block3a_dwconv_pad False\n",
            "50 block3a_dwconv False\n",
            "51 block3a_bn False\n",
            "52 block3a_activation False\n",
            "53 block3a_se_squeeze False\n",
            "54 block3a_se_reshape False\n",
            "55 block3a_se_reduce False\n",
            "56 block3a_se_expand False\n",
            "57 block3a_se_excite False\n",
            "58 block3a_project_conv False\n",
            "59 block3a_project_bn False\n",
            "60 block3b_expand_conv False\n",
            "61 block3b_expand_bn False\n",
            "62 block3b_expand_activation False\n",
            "63 block3b_dwconv False\n",
            "64 block3b_bn False\n",
            "65 block3b_activation False\n",
            "66 block3b_se_squeeze False\n",
            "67 block3b_se_reshape False\n",
            "68 block3b_se_reduce False\n",
            "69 block3b_se_expand False\n",
            "70 block3b_se_excite False\n",
            "71 block3b_project_conv False\n",
            "72 block3b_project_bn False\n",
            "73 block3b_drop False\n",
            "74 block3b_add False\n",
            "75 block4a_expand_conv False\n",
            "76 block4a_expand_bn False\n",
            "77 block4a_expand_activation False\n",
            "78 block4a_dwconv_pad False\n",
            "79 block4a_dwconv False\n",
            "80 block4a_bn False\n",
            "81 block4a_activation False\n",
            "82 block4a_se_squeeze False\n",
            "83 block4a_se_reshape False\n",
            "84 block4a_se_reduce False\n",
            "85 block4a_se_expand False\n",
            "86 block4a_se_excite False\n",
            "87 block4a_project_conv False\n",
            "88 block4a_project_bn False\n",
            "89 block4b_expand_conv False\n",
            "90 block4b_expand_bn False\n",
            "91 block4b_expand_activation False\n",
            "92 block4b_dwconv False\n",
            "93 block4b_bn False\n",
            "94 block4b_activation False\n",
            "95 block4b_se_squeeze False\n",
            "96 block4b_se_reshape False\n",
            "97 block4b_se_reduce False\n",
            "98 block4b_se_expand False\n",
            "99 block4b_se_excite False\n",
            "100 block4b_project_conv False\n",
            "101 block4b_project_bn False\n",
            "102 block4b_drop False\n",
            "103 block4b_add False\n",
            "104 block4c_expand_conv False\n",
            "105 block4c_expand_bn False\n",
            "106 block4c_expand_activation False\n",
            "107 block4c_dwconv False\n",
            "108 block4c_bn False\n",
            "109 block4c_activation False\n",
            "110 block4c_se_squeeze False\n",
            "111 block4c_se_reshape False\n",
            "112 block4c_se_reduce False\n",
            "113 block4c_se_expand False\n",
            "114 block4c_se_excite False\n",
            "115 block4c_project_conv False\n",
            "116 block4c_project_bn False\n",
            "117 block4c_drop False\n",
            "118 block4c_add False\n",
            "119 block5a_expand_conv False\n",
            "120 block5a_expand_bn False\n",
            "121 block5a_expand_activation False\n",
            "122 block5a_dwconv False\n",
            "123 block5a_bn False\n",
            "124 block5a_activation False\n",
            "125 block5a_se_squeeze False\n",
            "126 block5a_se_reshape False\n",
            "127 block5a_se_reduce False\n",
            "128 block5a_se_expand False\n",
            "129 block5a_se_excite False\n",
            "130 block5a_project_conv False\n",
            "131 block5a_project_bn False\n",
            "132 block5b_expand_conv False\n",
            "133 block5b_expand_bn False\n",
            "134 block5b_expand_activation False\n",
            "135 block5b_dwconv False\n",
            "136 block5b_bn False\n",
            "137 block5b_activation False\n",
            "138 block5b_se_squeeze False\n",
            "139 block5b_se_reshape False\n",
            "140 block5b_se_reduce False\n",
            "141 block5b_se_expand False\n",
            "142 block5b_se_excite False\n",
            "143 block5b_project_conv False\n",
            "144 block5b_project_bn False\n",
            "145 block5b_drop False\n",
            "146 block5b_add False\n",
            "147 block5c_expand_conv False\n",
            "148 block5c_expand_bn False\n",
            "149 block5c_expand_activation False\n",
            "150 block5c_dwconv False\n",
            "151 block5c_bn False\n",
            "152 block5c_activation False\n",
            "153 block5c_se_squeeze False\n",
            "154 block5c_se_reshape False\n",
            "155 block5c_se_reduce False\n",
            "156 block5c_se_expand False\n",
            "157 block5c_se_excite False\n",
            "158 block5c_project_conv False\n",
            "159 block5c_project_bn False\n",
            "160 block5c_drop False\n",
            "161 block5c_add False\n",
            "162 block6a_expand_conv False\n",
            "163 block6a_expand_bn False\n",
            "164 block6a_expand_activation False\n",
            "165 block6a_dwconv_pad False\n",
            "166 block6a_dwconv False\n",
            "167 block6a_bn False\n",
            "168 block6a_activation False\n",
            "169 block6a_se_squeeze False\n",
            "170 block6a_se_reshape False\n",
            "171 block6a_se_reduce False\n",
            "172 block6a_se_expand False\n",
            "173 block6a_se_excite False\n",
            "174 block6a_project_conv False\n",
            "175 block6a_project_bn False\n",
            "176 block6b_expand_conv False\n",
            "177 block6b_expand_bn False\n",
            "178 block6b_expand_activation False\n",
            "179 block6b_dwconv False\n",
            "180 block6b_bn False\n",
            "181 block6b_activation False\n",
            "182 block6b_se_squeeze False\n",
            "183 block6b_se_reshape False\n",
            "184 block6b_se_reduce False\n",
            "185 block6b_se_expand False\n",
            "186 block6b_se_excite False\n",
            "187 block6b_project_conv False\n",
            "188 block6b_project_bn False\n",
            "189 block6b_drop False\n",
            "190 block6b_add False\n",
            "191 block6c_expand_conv False\n",
            "192 block6c_expand_bn False\n",
            "193 block6c_expand_activation False\n",
            "194 block6c_dwconv False\n",
            "195 block6c_bn False\n",
            "196 block6c_activation False\n",
            "197 block6c_se_squeeze False\n",
            "198 block6c_se_reshape False\n",
            "199 block6c_se_reduce False\n",
            "200 block6c_se_expand False\n",
            "201 block6c_se_excite False\n",
            "202 block6c_project_conv False\n",
            "203 block6c_project_bn False\n",
            "204 block6c_drop False\n",
            "205 block6c_add False\n",
            "206 block6d_expand_conv False\n",
            "207 block6d_expand_bn False\n",
            "208 block6d_expand_activation False\n",
            "209 block6d_dwconv False\n",
            "210 block6d_bn False\n",
            "211 block6d_activation False\n",
            "212 block6d_se_squeeze False\n",
            "213 block6d_se_reshape False\n",
            "214 block6d_se_reduce False\n",
            "215 block6d_se_expand False\n",
            "216 block6d_se_excite False\n",
            "217 block6d_project_conv False\n",
            "218 block6d_project_bn False\n",
            "219 block6d_drop False\n",
            "220 block6d_add False\n",
            "221 block7a_expand_conv False\n",
            "222 block7a_expand_bn False\n",
            "223 block7a_expand_activation False\n",
            "224 block7a_dwconv False\n",
            "225 block7a_bn False\n",
            "226 block7a_activation False\n",
            "227 block7a_se_squeeze True\n",
            "228 block7a_se_reshape True\n",
            "229 block7a_se_reduce True\n",
            "230 block7a_se_expand True\n",
            "231 block7a_se_excite True\n",
            "232 block7a_project_conv True\n",
            "233 block7a_project_bn True\n",
            "234 top_conv True\n",
            "235 top_bn True\n",
            "236 top_activation True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMOBjdugk3t9"
      },
      "source": [
        "# Compile\n",
        "model_2.compile(loss=\"categorical_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n",
        "                metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1UBCIXNl0pA",
        "outputId": "c4c1df3b-754a-454e-fcfe-699b1e8d0913"
      },
      "source": [
        "# Continue to train and fine-tune the model to our data (100% of training data)\n",
        "fine_tune_epochs = initial_epochs + 5\n",
        "\n",
        "history_fine_10_classes_full = model_2.fit(train_data_10_classes_full,\n",
        "                                           epochs=fine_tune_epochs,\n",
        "                                           validation_data=test_data,\n",
        "                                           validation_steps=int(0.25 * len(test_data)),\n",
        "                                           initial_epoch=history_10_percent_data_aug.epoch[-1],\n",
        "                                           callbacks=[create_tensorboard_callback(dir_name=\"transfer_learning\",\n",
        "                                                                                  experiment_name=\"full_10_classes_fine_tune_last_10\")])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving TensorBoard log files to: transfer_learning/full_10_classes_fine_tune_last_10/20210218-052607\n",
            "Epoch 5/10\n",
            "235/235 [==============================] - 52s 203ms/step - loss: 0.7904 - accuracy: 0.7506 - val_loss: 0.4620 - val_accuracy: 0.8273\n",
            "Epoch 6/10\n",
            "235/235 [==============================] - 49s 204ms/step - loss: 0.5858 - accuracy: 0.8077 - val_loss: 0.3926 - val_accuracy: 0.8750\n",
            "Epoch 7/10\n",
            "235/235 [==============================] - 47s 200ms/step - loss: 0.5134 - accuracy: 0.8331 - val_loss: 0.3676 - val_accuracy: 0.8750\n",
            "Epoch 8/10\n",
            "235/235 [==============================] - 45s 191ms/step - loss: 0.4742 - accuracy: 0.8458 - val_loss: 0.3384 - val_accuracy: 0.8865\n",
            "Epoch 9/10\n",
            "235/235 [==============================] - 43s 181ms/step - loss: 0.4342 - accuracy: 0.8629 - val_loss: 0.3106 - val_accuracy: 0.8898\n",
            "Epoch 10/10\n",
            "235/235 [==============================] - 43s 180ms/step - loss: 0.4138 - accuracy: 0.8708 - val_loss: 0.3509 - val_accuracy: 0.8816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yViamjkQpA7r"
      },
      "source": [
        "> 🔑 **Note:** Fine-tuning generally takes longer per epoch (more layers being updated) and in the case of the model we just ran we used 10x more data than before (more patterns to find) so it makes sense training took longer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnJZCVQJnsDM",
        "outputId": "5d4bda8e-43e2-4dca-957d-07c0093da47d"
      },
      "source": [
        "# Let's evaluate on all of the test data\n",
        "results_fine_tune_full_data = model_2.evaluate(test_data)\n",
        "results_fine_tune_full_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 10s 118ms/step - loss: 0.3116 - accuracy: 0.9012\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3115694522857666, 0.901199996471405]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "GY8eOuohoFjU",
        "outputId": "c3488ff0-61d6-4b66-ae2a-2d510f456cd4"
      },
      "source": [
        "# How did fine-tuning go with more data?\n",
        "compare_historys(original_history=history_10_percent_data_aug,\n",
        "                 new_history=history_fine_10_classes_full,\n",
        "                 initial_epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lhQEDBpCQkEBcXBxLlixx2uesKxpkUAf6WqPAdx4+yb+1C1wppYoICwujZ8+ezJ8/H7Ba09dccw0iwjPPPENSUhJr1qxh0aJFhSFbGY899hjdunVjzZo1PPvss9x4440AvPTSS7zxxhusWrWKJUuW4O/vz8cff8wll1zCqlWrWL16NQkJCU79rHVBg72mZt9zIhjXqyXvLt3J0Ljm9GjV2NUlKaXU2eY/BAfWOnebkfEwbHKZixR0f48cOZKZM2fy3nvvAfDpp58ydepUcnNzSUlJYcOGDXTt2rVSu1+6dGnhvasHDx5MWloax44do1+/ftx3332MGzeO0aNHExMTw3nnncfNN99MTk4Oo0aNapBB3SBb1AUeHt6ZqBB/Hpi1WrvAlVLKwciRI/nxxx9ZuXIlmZmZ9OjRg507d/LSSy/x448/smbNGkaMGFHq7S2r4qGHHuLdd9/l1KlT9OvXj02bNjFgwAAWL15MdHQ048eP58MPP3Ta/uqKBtuiBgjytUaBX//e77z8wxYeHqYXwVdKuZlyWr41JSgoiEGDBnHzzTcXDiI7duwYgYGBhISEcPDgQebPn8/AgQMrve3zzz+fjz76iEcffZSff/6ZiIgIGjVqxPbt24mPjyc+Pp7ly5ezadMm/P39iYmJ4ZZbbuH06dOsXLmysKu8oWjQQQ3Qv30EY3u24J3FOxgaG0m3ltoFrpRSYHV/X3HFFYUjwM8991y6detGp06daNGiBf369avQdkaMGIG3tzcAffr04e233+bmm2+ma9euBAQEMH36dMA6BWzhwoV4eHgQGxvLsGHDmDlzJi+++CLe3t4EBQU1yBZ1pW5zWVucdZvLijqelcMlLy8mwNeLr+/pj5+3Z63tWzUMeptLVRl6m8v6rcZuc1mfBft589yVXdl26ASv/rjV1eUopZRShTSobRd0aMK1iS14e9F2Vu896upylFJKKUCDuoh/XtqZZo38uP+z1ZzO1VHgSimlXE+D2kEjP2+eHR3P1kMneO3Hba4uRymllNKgLm5Qx6Zc3SOGNxdtZ21yhqvLUUop1cCVG9QiMk1EDonIulLmPyAiq+zHOhHJE5Ewe94uEVlrz6szt0955NIuRAT5cP9nq8nOzXd1OUoppRqwirSoPwCGljbTGPOiMSbBGJMAPAwsMsY43pZqkD2/xGHn7ijE35vnRsez+eBxXv9JR4ErpRqeZ555htjYWLp27UpCQgK///47YJ3rnJmZWentffDBB+zfv7/EeePHj6dNmzaFN+SYMmUK//rXv/jhhx+q9RnWrl1buM2wsLDCfVx00UWV3tbw4cM5etQ1A43LveCJMWaxiLSu4PbGAjOqU5C7GNypGaO7R/PGz9sZEhtJXHSIq0tSSqla8dtvv/H111+zcuVKfH19OXz4cOGdtF555RWuv/56AgICKry9vLw8PvjgA+Li4oiKiipxmRdffJGrrrrKKfUXiI+PZ9WqVYD1Y+DSSy+t8j7mzZvnzNIqxWnHqEUkAKvlPdthsgG+E5EVInKrs/ZVWx67NJbwQO0CV0o1LCkpKURERODr6wtAREQEUVFRTJkyhf379zNo0CAGDRoEUOptL1u3bs2kSZPo3r07M2bMICkpiXHjxpGQkMCpU6fKrWH8+PHMmjWrcFuPPfYY3bt3Jz4+nk2bNgFw8uRJbr75Znr27Em3bt348ssvK/T5Bg4cSMFFtQ4fPkzr1q0Bq9U/evRohg4dSvv27XnwwQeLfJ7Dhw+za9cuOnfuzC233EJsbCxDhgwp/DzLly8v7IF44IEHiIuLq1A95XHmYLLLgF+KdXv3N8Z0B4YBd4nIgNJWFpFbRSRJRJJSU1OdWFbVhQR48+wV8Ww6cJw3FuoocKVUwzBkyBD27t1Lhw4duPPOO1m0aBEAEydOJCoqioULF7Jw4UKAMm97GR4ezsqVK7n++utJTEzko48+YtWqVfj7+5+1zwceeKCwm3rt2rPvFhYREcHKlSu54447eOmllwr3PXjwYP744w8WLlzIAw88wMmTJ6v12VetWsUnn3zC2rVr+eSTT9i7d+9Zy2zdupW77rqL9evXExoaWngnsAkTJvD222+zatUqPD2dd4VLZ17rewzFur2NMfvs50Mi8jnQE1hc0srGmKnAVLAuIerEuqrloi7NuKJbNG8s3MYlsZF0iWrk6pKUUg3I8388z6b0TU7dZqewTkzqOanU+UFBQaxYsYIlS5awcOFCrr32WiZPnsz48ePPWras215ee+21Fa6pvK7v0aNHA9CjRw/mzJkDwHfffcfcuXMLgzsrK4s9e/ZU6/KrF154ISEh1qHOLl26sHv3blq0aFFkmYJj3QX17Nq1i6NHj3L8+HH69OkDwHXXXcfXX39d5TocOSWoRSQEuAC43mFaIOBhjDluvx4CPOmM/dW2xy7rwpKth7n/s9V8eXc/vD31rDalVP3m6enJwIEDGThwIPHx8UyfPv2soC647eXy5ctp3Lgx48ePL3Lby8DAQKfVU9AN7+npSW5uLgDGGGbPnk3Hjh0rtS0vLy/y863DmcVv01mwn+L7KmuZinTlV0e5QS0iM4CBQISIJAOPAd4Axpi37MWuAL4zxjj2OTQDPheRgv18bIxZ4LzSa09ogA/PXhHHrf9dwZs/b2fihe1dXZJSqoEoq+VbUzZv3oyHhwft21v/161atYpWrVoBEBwczPHjx4mIiKjUbS8L1nOmSy65hNdee43XXnsNEeHPP/+kW7du5a7XunVrVqxYQc+ePQuPg1dXaGgowcHB/P777/Tq1avwjmPOUJFR32MrsMwHWKdxOU7bAZxb1cLczZDYSC4/N4rXftrKxV2a0bm5doErpeqnEydOcM8993D06FG8vLw455xzmDp1KgC33norQ4cOLTxWXdHbXo4fP57bb78df39/fvvttxKPU1fWo48+yt///ne6du1Kfn4+bdq0qVB38/33388111zD1KlTGTFiRLXrKPDee+9xyy234OHhwQUXXFDYhV5depvLSkg/mc2QlxcRGeLH53dqF7iqOL3NpaoMvc1l3XTixAmCgoIAmDx5MikpKbz66qtnLae3uaxBYYE+PD0qjnX7jvH2ou2uLkcppZQb+eabb0hISCAuLo4lS5bwyCOPOGW7zhz13SAMjWvOpV2b8+qPW7m4SyQdI4NdXZJSSik3cO2111ZqpHtFaYu6Cp64PJZGft48MGs1uXl6IRSllFI1R4O6CsKDfHlqVBxrkjOYumSHq8tRStVD7jh+SFVfVf5cNairaHh8c0bEN+eV77ey9aBzTzlQSjVsfn5+pKWlaVjXM8YY0tLS8PPzq9R6eoy6Gp4YGctvO9K4f9YaZt/eBy8dBa6UcoKYmBiSk5Nxl8spK+fx8/MjJiamUutoUFdDRJAvT1weyz0z/uTdpTu5/YJ2ri5JKVUPeHt706ZNG1eXodyENgGr6dKuzRkaG8n/+34L2w5pF7hSSinn0qCuJhHhqVFxBPh48sCsNeTl6zElpZRSzqNB7QRNgq0u8D/3HGXa0p2uLkcppVQ9okHtJJefG8WQLs146bvNbE894epylFJK1RMa1E4iIjx9RRx+3p48qF3gSimlnESD2omaBvvxxOWxrNh9hPd/0S5wpZRS1adB7WQjE6K4qHNTXvx2MzsPnyx/BaWUUqoM5Qa1iEwTkUMisq6U+QNFJENEVtmPfznMGyoim0Vkm4g85MzC3ZWI8MwV8fh6efDAZ6u1C1wppVS1VKRF/QEwtJxllhhjEuzHkwAi4gm8AQwDugBjRaRLdYqtkoxkyMmq1V02a+THY5fFkrT7CNN/3VWr+1ZKKVW/lBvUxpjFQHoVtt0T2GaM2WGMyQZmAiOrsJ2qy86EacPg0xsgN7tWdz26ezSDOzXlhW83sUu7wJVSSlWRs45R9xGR1SIyX0Ri7WnRwF6HZZLtabXHJwDOvw+2fgezJkBeTq3tWkR49op4vD09eHD2GvK1C1wppVQVOCOoVwKtjDHnAq8BX1RlIyJyq4gkiUiSUy9EnzgBhj4Pm76Gz2+D/DznbbsckSF+/OvSLvyxM53/Lttda/tVSilVf1Q7qI0xx4wxJ+zX8wBvEYkA9gEtHBaNsaeVtp2pxphEY0xikyZNqltWUb1vh4uegHWz4cu7IT/fudsvw1U9YhjYsQmT529iT1pmre1XKaVU/VDtoBaRSBER+3VPe5tpwHKgvYi0EREfYAwwt7r7q7L+f4eB/werP4Zv7oNaus+riPDc6Hi8PIQHZ6/WLnCllFKVUpHTs2YAvwEdRSRZRP4iIreLyO32IlcB60RkNTAFGGMsucDdwLfARuBTY8z6mvkYFXTBg9D/XljxPix4uNbCunmIP49c2pllO9L56HftAldKKVVx5d6P2hgztpz5rwOvlzJvHjCvaqXVABG48DHIPQ3L/gNevnDR49b0GnZNYgu+XpPCc/M3MbBjU1qEBdT4PpVSStV9De/KZCJwybOQeDP88goser6WditMvrIrHiJMmr0GU0uteaWUUnVbwwtqsMJ6+L8hYRz8/BwsfblWdhsd6s8/R3Tm1+1pfPzHnlrZp1JKqbqtYQY1gIcHXP4axF0FPzwOy96sld2OOa8F/c+J4NlvNpJ8REeBK6WUKlvDDWoAD0+44i3ofBkseAiSptX4Lq0u8HgAHpq9VrvAlVJKlalhBzWApzdcOQ3aXwJf3wurPq7xXcY0DuDh4Z1Zuu0wM5fvLX8FpZRSDZYGNYCXD1zzIbQdCF/eBWtn1fgur+vZkr7twnnmm43sO3qqxvenlFKqbtKgLuDtB2NmQMs+MOdW2PhVje7Ow0N4/squ5BvDQ3otcKWUUqXQoHbkEwDXfQLR3eGzCbDluxrdXYuwAB4e1oklWw/z1w+TOHKydu/wpZRSyv1pUBfnGwzjZkGzLvDJ9bB9YY3u7vrerXji8liWbj3M8ClLWLG7KncUVUopVV9pUJfEPxRu+ALCz4EZY2HXLzW2KxHhpr6tmX1HX7w9Pbjm7WW8tWi7doUrpZQCNKhLFxAGN34JoS3g42tg7/Ia3V18TAhfT+zPJbHNmDx/E3+Zvpx07QpXSqkGT4O6LEFN4Ma5ENgE/ncl7F9Vo7tr5OfNG9d158mRsfyyLY0RU5aQtEu7wpVSqiHToC5Po+Zw01fgFwL/HQUHa/YGYCLCjX3OdIVfO1W7wpVSqiHToK6I0BZw05fg5Q8fjoTULTW+y4Ku8KGxkUyev4mbtStcKaUaJA3qigprCzfNBQQ+vBzSd9T4Lhv5efP6dd14amQsv25LY/ir2hWulFINTblBLSLTROSQiKwrZf44EVkjImtF5FcROddh3i57+ioRSXJm4S4R0d4aYJZ7GqZfDkdr/g5YIsINfVoz586++HpbXeFv/qxd4Uop1VBUpEX9ATC0jPk7gQuMMfHAU8DUYvMHGWMSjDGJVSvRzTTrAjd+AaePwfTL4Nj+WtltXHQIX91jdYU/v0C7wpVSqqEoN6iNMYuBUvtbjTG/GmOO2G+XATFOqs19NT8Xrp8DJ9OslvWJQ7Wy25K6wpdrV7hSStVrzj5G/RdgvsN7A3wnIitE5FYn78u1YhJh3GdwbJ81wOxkWq3stnhX+Jipy/jPz9u0K1wppeoppwW1iAzCCupJDpP7G2O6A8OAu0RkQBnr3yoiSSKSlJqa6qyyalarPjB2pjWw7L+j4NTRWtt1XHQIX9td4S8s2MyED7QrXCml6iOnBLWIdAXeBUYaYwqblsaYffbzIeBzoGdp2zDGTDXGJBpjEps0aeKMsmpH2wvg2o/g0Ebroiinj9faroMLusJHxfHbdu0KV0qp+qjaQS0iLYE5wA3GmC0O0wNFJLjgNTAEKHHkeJ3X/iK4ZjqkrIKProHsk7W2axHhht6tmHNnX/y0K1wppeqdipyeNQP4DegoIski8hcRuV1EbrcX+RcQDvyn2GlYzYClIrIa+AP4xhizoAY+g3voNAJGvwN7l1k38sg5Vau7LxwVHnemKzztxOlarUEppZTzeZW3gDFmbDnz/wr8tYTpO4Bzz16jHosbDXnZ8Pnt8OmNVpe4l0+t7T7Yz5vXx3ajT9twnvx6AyOmLGXK2G70bBNWayE36TAAACAASURBVDUopZRyLr0ymbOdOwYuewW2fgezJkBeTq3uXkS4vncr5txhdYWPfWcZbyzUrnCllKqrNKhrQo/xMOwF2PQ1fH4b5OfVegkFXeHD4iJ58VvtCldKqbpKg7qm9LoNLn4S1s2GL++G/PxaLyHYz5vXxnbj6VFx/LYjjeFTlvDHTh0VrpRSdYkGdU3q9zcY9E9Y/TF8cx+Y2u9+LugK//zOvvh7e2pXuFJK1TEa1DVtwAPQ/z5Y8T4seNglYQ0QG1W0K3y8doUrpVSdoEFd00Tgwn9B77vg9zfhh8ddFtYFXeHPXBHHMrsr/PcdtXPpU6WUUlWjQV0bROCSZyDxL/DLK7DoeReWIozrZXWFB/h4aVe4Ukq5OQ3q2iICw1+CbtfDz8/B0pddWk5sVAhz7+7HiK5RvPjtZm56/w/tCldKKTekQV2bPDzgsikQf7XVBb7sTZeWE+znzZQxCTx7RTy/70zXrnCllHJDGtS1zcMTRr0FnS+HBQ9B0jSXliMiXNerJV/c2a+wK/z1n7ZqV7hSSrkJDWpX8PSCK9+DDkPh63th1ceuroguUY346p7+XNo1ipe+28JN7//BYe0KV0opl9OgdhUvH7h6OrQdBF/cCTPHwZ7fXVpSkK8Xrzp2hb+6hGXaFa6UUi6lQe1K3n4w5mMYcD/sWgrThsC7F8PGr1xy2VEo2hUe5OvFddoVrpRSLqVB7Wo+ATD4Ebhvg3V98BMH4JPr4fXzYPl7tX67zAJdohox16ErfOQbv/C/Zbs5mpntknqUUqqh0qB2Fz6B1vXB7/kTrnof/BpZlx19OQ5+fh5O1n4XdEFX+EtXn0t2bj6PfLGOns/8yB3/W8EPGw6Sk1f71y9XSqmGptz7UQOIyDTgUuCQMSauhPkCvAoMBzKB8caYlfa8m4BH7EWfNsZMd0bh9Zanl3Vf69grrO7wX1+Dn5+1zrvuNg763AVhbWutHBHhqh4xXNk9mvX7jzFn5T6+XLWP+esOEBbow+XnRnFl9xjiohth/TVQSinlTGIqcDlLERkAnAA+LCWohwP3YAV1L+BVY0wvEQkDkoBEwAArgB7GmCNl7S8xMdEkJSVV9rPUX4c2wm+vw5pPrftbd77MuuFHTKJLysnJy2fJ1lRmr9jH9xsPkp2bT4dmQYzuHsOohGgiQ/xcUpc7m7BgAgDvD33fxZUopdyRiKwwxpT4n3qFWtTGmMUi0rqMRUZihbgBlolIqIg0BwYC3xtj0u1CvgeGAjMqXr6iaWcY+QYMfhR+fwuWT4ONc6FlX+g3EdpfYl1MpZZ4e3owuFMzBndqRkZmDt+sTWHOymQmz9/E8ws20f+cCEZ3j+aS2EgCfCr0V0wppVQpnPW/aDSw1+F9sj2ttOmqKoIj4aLH4fx/wMr/wrL/wIwxENEB+twNXa+1RpLXopAAb67r1ZLrerVk1+GTzPlzH3NWJnPvJ6sJ9FnHsPjmjO4eTe824Xh4aNe4UkpVlts0d0TkVuBWgJYtW7q4GjfnGwx97oSet8D6L+DXV+GrifDT09DrVuvmHwFhtV5W64hA7ru4A3+/sD1Ju48wZ2Uy36xJYdaKZKJC/LiiezSju8fQrklQrdemlFJ1lbP6S/cBLRzex9jTSpt+FmPMVGNMojEmsUmTJk4qq57z9IauV8NtS+DGLyEy3grrl+Ng/iQ4ssslZXl4CD3bhDH5yq4sf+QipoztRofIYN78eTsX/nsRI9/4hQ9/28WRk3qql1JKladCg8kA7GPUX5cymGwEcDdnBpNNMcb0tAeTrQC624uuxBpMll7WvnQwWTUcWGcNPFv7GZh86DLKOo4d1c3VlXHoWBZzV+9n1opkNh04jrenMLhTU0Z3j2FQx6b4eNXfswV1MJlSqizVHkwmIjOwBoZFiEgy8BjgDWCMeQuYhxXS27BOz5pgz0sXkaeA5famniwvpFU1RcbBFW+dGXi24gNYPwdanw99J0L7i61bbrpA00Z+/PX8tvz1/LZs2H+MOSuT+WLVfr5df5DGAd5cfm4Uo7vH0DUmRE/1UkopW4Vb1LVJW9ROlJUBK6Zbt9Q8vh+adIa+90D8VeDl6+rqyM3LZ8m2w8xekcx3G6xTvdo1CWR09xiu6BZNVKi/q0t0Cm1RK6XKUlaLWoO6ocjNtlrWv0yBQ+shKBJ63w49JoB/qKurAyDjVA7z16YwZ+U+/tiVjgj0aRvOld1jGBoXSaCv24x9rDQNaqVUWTSo1RnGwPaf4NcpsONn8AmC7jdB7zsgtEW5q9eWPWmZfP7nPub8mczutEz8vT0ZFhfJ6O4x9GkXjmcdO9VLg1opVRYNalWylNXw6+uwbrb1Pu5Kq1u8eVfX1uXAGMOK3UeYvXIfX6/Zz/GsXJqH+DGqWzRXdo/mnKbBri6xQjSolVJl0aBWZTu61zqGvXI6ZJ+AtgOtgWftBrts4FlJsnLy+GHjQeas3MeiLank5Ru6xoQwuls0l50bRXiQ64+5l0aDWilVFg1qVTGnjsKK92HZW9btNpvFWS3suCutc7bdSOrx08xdvZ85K5NZv/8YXh7CuS1C6dUmjN5tw+nRqrFbHdPWoFZKlUWDWlVO7mlYO8u6c1fqRmgUbR3H7jjMuqiKG7WyATYdOMZXq/fz6/Y01iZnkJtv8PIQ4mNC6N02nF5twkhsHUaQC4Nbg1opVRYNalU1xsDW762BZ7uWWNMaRUOHS6DDUGgzALzd6/Spk6dzWbH7CMt2pPH7znRW7z1Kbr7B00OIjw6hV1urxZ3YqjHBfrXXS6BBrZQqS7UveKIaKBHoMMR6HD8IW7+DLQtg9SeQNA28/KHtBVZwt78EQlx/v5VAXy8GdGjCgA7WZWgzs3NZufsoy3aksWxHGtOW7uTtRTvwEOzgDqd3W6vF3agWg1sppSpKg1pVTHAz6H6D9cg9DbuWwpZvYct8K7zB6hbvMMxqbUd1q9Vbb5YmwMeL/u0j6N8+AoBT2Xms3HOE33eksWxHOh/8soupi63gjo0KoXdBi7t1GCH+GtxKKdfTrm9VPcZA6iYrrLd8C3t/t64xHtjEamV3uATaDbLu+OWGsnKs4F62I51lO9JYteco2Xn5iEBsVCN6tQmnd9twerYOIySg6sGtXd9KqbJo17eqOSLQtLP16H8vZKbDth+s4N74Faz6H3j6QOv+Vku7/RAIa+Pqqgv5eXvSt10EfdtZLe6snDz+3HOU33daXeX/Xbab95buRAQ6RzayBqe1DaNXmzBCA3xcXL1SqiHQFrWqOXk5Vgt783yrtZ221ZrepNOZAWkxPcHTfX8vZuXksXrvUZbtSOf3nWms2H2E07lWi7tjs2B6tw0vHFneOLD04NYWtVKqLDrqW7mHtO32ce0FsPsXyM8Fv1Drjl4dhsI5F4J/Y1dXWabTuXms3pthHeO2gzsrJx+ATpHBhedx92wTVuQCLBrUSqmyaFAr95OVAdsXWsG99VvITAPxhJZ9zrS2I9q73TnbxWXn5rMm+Si/77SOcSftOsKpnDwAOjQLslvb4czY+xDenh4a1EqpEmlQK/eWnwf7VpwZkHZwnTW9cRsrsDsOhZZ9wcv9jwln5+azdl9G4XncSbvSyczOw7/l2/h5e9LD55+c0zSoyMOVF2JRSrmHage1iAwFXgU8gXeNMZOLzX8ZGGS/DQCaGmNC7Xl5wFp73h5jzOXl7U+DuoE7utdqZW/5FnYsgrzT4BMM5wy2u8gvhqAmrq6yQnLyrOB++Nc7OHE6F/+0e9h1+CS5+Wf+3UU28isM7XZNgzinifU6IsgHcfMeBaWUc1QrqEXEE9gCXAwkA8uBscaYDaUsfw/QzRhzs/3+hDEmqDIFa1CrQtknYefiMwPSThwABGISz3SRN4tz+y5yx2PUOXn57E7LZNuhE2xPPcG2QycKX2dm5xWuE+LvbQV4k6It8OhQfzzq2G0+lVJlq+7pWT2BbcaYHfbGZgIjgRKDGhgLPFaVQpU6i0+gdY3xjsOsc7ZTVp8ZkPbT09ajUYw1IK1pZwiJsR8trIFpbhjg3p4ehaHryBhDSkZWYXBvs0P8h40H+SRpb+Fyft4etI0IOqsLvXV4ID5err/IjFLKuSoS1NHAXof3yUCvkhYUkVZAG+Anh8l+IpIE5AKTjTFfVLFW1dCJQFSC9Rg4qehlTdd+Zt2i05F3wJngbhRthXdhkNvTvP1c81lKICJEhfoTFepfeAnUAkdOZhcGd8Fjxe4jzF29v3AZTw+hVViA1X3u0BJvp8fBlarTnP2vdwwwyxiT5zCtlTFmn4i0BX4SkbXGmO3FVxSRW4FbAVq2bOnkslS95HhZ0/x8yDwMGXshYx9kJNuPvdbzgXVw8tDZ2whsUrQVXhji9nNgE7e4FGrjQB/OCwzjvNZhRaZnZueyI/VkkQDflnqChZsOFTkO3jzEOg7erlg3enigHgdXyt1VJKj3AS0c3sfY00oyBrjLcYIxZp/9vENEfga6AWcFtTFmKjAVrGPUFahLqTM8PCCoqfWI7lHyMrmn4ZhjiCefCfbULbDtR8jJLLqOp4/dGi8I8uiiwd4oGnwrNQTDqQJ8vIiLDiEuOqTI9ILj4AXHwLfbAf5p0t4ix8FDA7yLHAPv3Tac2KhGGt5KuZGKBPVyoL2ItMEK6DHAdcUXEpFOQGPgN4dpjYFMY8xpEYkA+gEvOKNwpSrNyxfC2lqPkhgDp46cCfFj+860yDOSYeciOJ5iXcvckX/jMy3wIg872IMia/6zFeN4HPyS2DPTSzsO/v2Gg8xcbh3hahkWwLD4SEbENyc+OkRDWykXKzeojTG5InI38C3W6VnTjDHrReRJIMkYM9dedAww0xQdRt4ZeFtE8gEPrGPUpQ1CU8q1RCAgzHo071ryMnk5cPzA2V3rBa/3/GpdzKXIdj0hOgp8giB1MzTpWPOfpRRlHQdPPX6anzYdZN7aA7y3xLodaHSoP8PjIxkW35yEmFAdba6UC+gFT5RytqxjDl3sVtf6hJQFkJnO+wcOQe874IJJbntHMYCMzBy+33iQeWtTWLI1lZw8Q/MQP4bFNWd4fCTdWzbW0FbKifTKZEq52IQFEyAvh/dzQ+HP/1rd4UOegvir3fIUMkfHsnL4caPV0l60JZXs3HyaBvsyLC6S4fHNSWwdhqeGtlLVokGtlIsVuSlH8gqY9w/Y/6d1adThL0JknIsrrJjjWTn8tOkQ89ceYOHmQ5zOzSciyJehcc0YHtecnm3C8PJ0/Sh5peoavR+1Uu4kpgf89SerZf3D4/D2+XDeLTDo/8A/1NXVlSnYz5uRCdGMTIjm5OlcFm62Qnv2in38b9kewgJ9uCS2GcPjm9O7bTjeGtpKVZsGtVKu4OEBPW6CzpfBwmdg+TuwbjZc9DgkjHOLc7fLE+jrxaVdo7i0axSnsvNYtOUQ89YeYO6q/cz4Yy+hAd4M6dKMYfHN6dcuQq+aplQVaVAr5UoBYTDi39D9Rpj3AMy9G1a8D8Nfgujurq6uwvx9PBka15yhcc3Jyslj8ZZU5q87wPy1B/g0KZlGfl5c3CWS4fGR9G8fga+Xp6tLVqrO0KBWyh00Pxdu/hbWfALfPQrvDLbC+8LHIDDc1dVVip+3J0NiIxkSG8np3Dx+2XaYb9Yc4PsNB5i9MplgXy8u7NyU4fHNGdChCX7eGtpKlUWDWil3IQLnjoGOw2HR87DsTdjwJVz4KPSYAB51L9B8vTwZ3KkZgzs1Izs3nl+3H2b+2gN8u+EAX6zaT6CPJ4M7N2N4XCQDOzbF36fufUalapqO+laqFhQZ9V1RhzZa3eG7lkBkPAz/N7Qs8X44dU5OXj7LdqQxb+0Bvl1/gPST2fh7ezK4U1OGxUcyqGNTAvVGIqoB0VHfStVFTTvDTV/B+s/hu0dg2hA4dyxc9IR1Q5I6zNvTg/PbN+H89k14amQsf+xKZ97aFBasO8g3a1Pw9fJgYMcmDI9vzuBOTQn283Z1yUq5jAa1Uu5MBOJGQ/shsOTf8OtrsPFrGPQw9LwVPOt+gHl5etC3XQR920XwxOVxJO1KtwairUvh2/UH8fHyYED7JgyPj+TCzs0I8a/7n1mpytCub6VqQZW6vktyeBssmATbfoAmnWH4C9BmgBMqdD/5+YaVe44wb60V2ikZWQA0a+RLy7AAWoYF0jIsgFbhAbSwn/W2naqu0q5vpeqLiHNg3CzYPA8WPATTL4PY0TDkaetOXfWIh4eQ2DqMxNZhPDKiM6uTj7J062F2p2eyJz2TX7YdZvaxrCLrBPp40iIsoDDAW4YF0DI8kFZhAUSF+uu53KpO0qBWqq4RgU4joN1g+OVVWPoybFkAAx6APndZt/OsZzw8hG4tG9OtZeMi07Ny8kg+YgX37jTreU9aJjsPn2TRllRO5565JamHQFSof9FWuN0qbxkeoF3qym1pUCtVV3n7w8CHrFO6vv0n/PgE/Pk/GPYCtL/I1dXVCj9vT85pGsw5Tc++E1l+viH1xGmHAD9pBXp6Jt+tP0jayewiy4f4ezsEeEBhgLcMC6B5iL/eeES5jAa1UnVd49Yw5iPY+gPMfxA+uhI6joChz1rzGigPD6FZIz+aNfKjZ5uws+afOJ3LnoIQTz9Z2Cpfvy+Db9cdIDf/zPgdb08hprEd3sWOi7cMCyDAR/8rVTWnQn+7RGQo8CrgCbxrjJlcbP544EVgnz3pdWPMu/a8m4BH7OlPG2OmO6FupVRx7S+CNr/Bb2/A4hfhjV7Q/17o9zer9a2KCPL1oktUI7pENTprXm5ePikZWXaIWwG+Nz2T3eknWbnnCMezcossHxHkS8swf1qFBxLT2J/IED+iQvxpHupH8xB/Gvl56SA3VWXlBrWIeAJvABcDycByEZlrjNlQbNFPjDF3F1s3DHgMSAQMsMJe94hTqldKFeXlC+ffB12vtc69/vk5WPURDJ1sXfFMw6JCvDw9aBFmtZr7lTD/aGb2WcfF96Rn8sfOdL5cdYr8YifTBPp40jzUn+YhfvbDnyg7xJuH+NE81J8gvcCLKkVF/mb0BLYZY3YAiMhMYCRQPKhLcgnwvTEm3V73e2AoMKNq5SqlKiQkGq5+HxInwLwHYeZ1cM5FMPR5a+S4qpbQAB9CA3zoGnP2bUlz8/I5dPw0KRlZpGScIuVoFvvt55SMU2w+cJzUE6cpfmZssJ9XkVZ4QahHFQa8v15itYGqSFBHA3sd3icDJV3H8EoRGQBsAe41xuwtZd36dQ6JUu6szQC4fQn88Y7Vuv5Pb+h7tzVC3CfQ1dXVS16eHkSF+hMV6g80LnGZ7Nx8Dh7LKgzz/UezOJBxiv32+7XJGWcNdgMIDfC2WuMhfsUC3WqhR4b46Z3J6iFn9bV8BcwwxpwWkduA6cDgymxARG4FbgVo2bKlk8pSSuHpDX3uhLgr4YfHrdO51nxqnXsde4V2h7uAj9eZrvXSZOXkcfBYFvvtlnhKRhb7j9rPGVms2HOEo5k5Z60XEeRD85CC4+R+hV3uUaH+RDaywtzbU88nr0sqEtT7gBYO72M4M2gMAGNMmsPbd4EXHNYdWGzdn0vaiTFmKjAVrCuTVaAupVRlBDeDK96EHjfBvPth1gRImgbDX7SuK67cip+3J63CA2kVXnrPR2Z2rtUqt7vXDzi00HennWTZ9jSOny468E3EGvzWPMQaEe/4XBDkkSF+OpLdjVTkT2I50F5E2mAF7xjgOscFRKS5MSbFfns5sNF+/S3wrIgU9P8MAR6udtVKqapr2RtuXQQr3ocfn4I3+0Gv22HgJPALcXV1qhICfLxo1ySIdk2CSl3meFYOB+xWeMpRq3v9YEYWKcey2JOWye870jhWbBQ7QCM/Lzu0/Yls5Gs/Fw320ABvHc1eC8oNamNMrojcjRW6nsA0Y8x6EXkSSDLGzAUmisjlQC6QDoy3100Xkaewwh7gyYKBZUopF/LwhPP+Cl2ugJ+ehGX/gbWfWTcAadIRmnSyHgFnn3+s6pZgP2+C/bxp3+zsi8IUyMzO5UBGFgeOZRV9tl9vSjlW4gA4Xy8PIktqkTu0zJsE+eKlXe3VojflUKoWOO2mHDVl30rr+HVyEuScPDM9sMmZ0HYM8MAIPbbdwOTk5ZNqj2YvGAhX+JyRRcqxUxzMOE12Xn6R9TwEmgb70SzEj+YOAV481P28G/YgOL0ph1KqbNHd4aa5kJ8Px/ZB6iaHx2ZY8wmcPnZmef+wYuHd0TrOHdRMA7ye8i4ymr1kxhjST2aX2jLfnnqCX7YdPuu4OVgj2gtCOzzQlwAfT/x9PPH39iz22gt/Hw/8vb0I8LHm+dnLBPh44eftUe+64zWolVJneHhAaAvr0f7iM9ONgeMpZ4I7dRMc2gTrP4eso2eW8ws5O8CbdIJG0RrgDYCIEB7kS3iQL7FRpY93OHHa6mov2jI/xYGM0xw4doqtB09wKiePzOxcsnLyS91OaQrC/UyAFw/6kn4AeOLvY4W/v7c1vehrL/y9PV3yQ0CDWilVPhFoFGU92jmceWkMnDhUNMBTN8OmebDywzPL+QRDkw4O3eh2iIe0sH4cqAYlyNeLc5oGcU7T0gfBFcjPN2Tl5pGZncepbPvZDvFTha/zir3OLXH64RPZZGZnkpWTT2Z2LpnZeUXusFYRItYPgYeHd+aG3q2q+hVUiga1UqrqRKzTvoKbQdsLis47ebho93nqJtj2g3VJ0wLeARDR4exWeOPW1oA31eB5eAgBPl41drpYfr4pDPIs+znTDnrHHwaFr+15HSrwI8NZNKiVUjUjMAIC+0Pr/kWnZ6bD4S1nus9TN8HOxbBm5pllPH2tAG9abBBb4zbgqf9tKefx8BACfb0IdONrrbtvZUqp+ikgzDqXu2XvotOzMiB1S9FW+J7frdPGCgn4NgK/Rtbx8ILXvvb7wtcFz6FFp/mFWK14PV6u6hANaqWUe/ALgRbnWQ9Hp0/A4c1WcKfvtAL99DHIOma9PrYPsjba0zLAlHPMUTyLhXdosXAv6UdAscD38tOwV7VGg1op5d58gyC6h/UojzGQfbJokJ8+dna4F7wumHdkV9FplHN9CQ/vMlrwIeDfGPxDiz03PjNfj7+rStCgVkrVHyJWsPsGWSPUqyI/H7KPOwR5SYGfUXT+6WNwYtuZZR0vGnN2kSWEuR3iBa+LzyuY7+1Xtc+knMsYyM+rtfESGtRKKeXIw8NuLVfjuue5p+HUUTh1xDrP/NQR++Hw2nH6kd1nppXVde/lX0KIhxYL+RIC3reRngZXEbnZcDIVThywTjs8bj+f9f4gDH3WugxvLdCgVkopZ/PyPXPaWmXk59ut9PLC3X5O33lmXu6p0rcrHnaYhzq03kMduupDi05zfPYJrtshb4z1nR4/aAVswaOkED5Vyq0o/MOsq+4FN4PwdtbrZvG19hE0qJVSyl14eNhhGmqdS14ZOVlnB/lZLXqHaUd22fOOgskrfbviYbXIywpzv5CS59Xk8fi8XDhpt27Pav0eLBrMuVlnr+/pYwVuUDPrtL+Wvc+8LwjloGYQ2BS8fGrmM1SQBrVSStUH3n7gHQnBkZVbzxjIPmEFdtZR6xh7wevSno/tP/M6L7vs7fvahxH8SwnzwlB3eA12yJbRBZ2ZRomD/vxCre8gqCm06GU9B0faAdwUgux5/o3rzMh9DWqllGrIRMA32HrQonLrGgM5p0oI84zSg/7w1jM/Bsrqrnfk4X0maENbQkxisdavHb5BzazDDvWMBrVSSqmqEQGfAOtRlVH2BYPuioc5pmgI+4XW7ePk1aRBrZRSyjWqOuiuganQTxQRGSoim0Vkm4g8VML8+0Rkg4isEZEfRaSVw7w8EVllP+Y6s3illFKqviu3RS0insAbwMVAMrBcROYaYzY4LPYnkGiMyRSRO4AXgGvteaeMMQlOrlsppZRqECrSou4JbDPG7DDGZAMzgZGOCxhjFhpjMu23y4AY55aplFJKNUwVCepoYK/D+2R7Wmn+Asx3eO8nIkkiskxERpW2kojcai+XlJqaWoGylFJKqfrPqYPJROR6IBFwvIN8K2PMPhFpC/wkImuNMduLr2uMmQpMBUhMTCznivhKKaVUw1CRFvU+ip5cF2NPK0JELgL+CVxujDldMN0Ys89+3gH8DHSrRr1KKaVUg1KRoF4OtBeRNiLiA4wBiozeFpFuwNtYIX3IYXpjEfG1X0cA/QDHQWhKNQidwjrRKayTq8tQStVB5XZ9G2NyReRu4FvAE5hmjFkvIk8CScaYucCLQBDwmViXZNtjjLkc6Ay8LSL5WD8KJhcbLa5UgzCp5yRXl6CUqqPEGPc7HJyYmGiSkpJcXYZSSilVK0RkhTEmsaR5DfeabEoppVQdoEGtlFJKuTENaqWUUsqNaVArpZRSbkyDWimllHJjGtRKKaWUG3PL07NEJBXY7cRNRgCHnbi9hki/w+rT77D69Dt0Dv0eq8/Z32ErY0yTkma4ZVA7m4gklXZ+mqoY/Q6rT7/D6tPv0Dn0e6y+2vwOtetbKaWUcmMa1EoppZQbayhBPdXVBdQD+h1Wn36H1affoXPo91h9tfYdNohj1EoppVRd1VBa1EoppVSdVK+DWkSGishmEdkmIg+5up66SERaiMhCEdkgIutF5G+urqmuEhFPEflTRL52dS11kYiEisgsEdkkIhtFpI+ra6prRORe+9/xOhGZISJ+rq7J3YnINBE5JCLrHKaFicj3IrLVfm5ckzXU26AWEU/gDWAY0AUYKyJdXFtVnZQL/MMY0wXoDdyl32OV/Q3Y6Ooi6rBXgQXGmE7Aueh3WSkiEg1MBBKNMXGAJzDGtVXVCR8AQ4tNewj40RjTHvjRfl9j6m1QAz2BbcaYHcaYbGAmMNLFNdU5xpgUY8xK+/VxrP8co11bVd0jIjHACOBdV9dSF4lICDAAeA/AGJNtjDnq2qrqJC/AX0S8gABgv4vr+KBmSgAAAipJREFUcXvGmMVAerHJI4Hp9uvpwKiarOH/t3f3oFFEARDH/wPRwlgKQY2QFGIdKzGNGEvRzkoJYqtgJWhjayFiZyPaGASJAVMIWtgH8QNE7VSSC4lJpWClOBa7QjqNerx95/ya293idoo7Zu+9t3uDXNS7gaUN+z1SMH9F0hgwASyUTVKl68AF4HvpIJUaB9aB2+30wU1Jw6VD1cT2MnAVWARWgE+2H5dNVa0R2yvt9iow0s+TDXJRxz8kaTtwHzhv+3PpPDWRdBRYs/2sdJaKDQH7gRu2J4Av9Hm4cdC086jHaS56dgHDkk6WTVU/N7dO9fX2qUEu6mVgz4b90fZYbJKkLTQlPWN7rnSeCk0CxyR9oJmCOSzpTtlI1ekBPds/R3NmaYo7ft8R4L3tddtfgTngYOFMtfooaSdA+7rWz5MNclE/BfZKGpe0lWbRxHzhTNWRJJp5wbe2r5XOUyPbF22P2h6j+Rw+sZ1fMptgexVYkrSvPTQFvCkYqUaLwAFJ29rv9RRZkPen5oHpdnsaeNDPkw31881Lsv1N0lngEc3qxlu2XxeOVaNJ4BTwStLL9tgl2w8LZor/0zlgpr3wfgecLpynKrYXJM0Cz2nu5nhBnlD2S5LuAoeAHZJ6wGXgCnBP0hmaf3o80dcMeTJZREREdw3y0HdERET1UtQREREdlqKOiIjosBR1REREh6WoIyIiOixFHRER0WEp6oiIiA5LUUdERHTYD85jCcJLULc4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thI0mwkmob4w"
      },
      "source": [
        "## Viewing our experiment data on TensorBoard\n",
        "\n",
        "> 🔑 **Note:** Anything you upload to TensorBoard.dev is going to be public. So if you have private data, do not upload."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_pNazA6pdVX"
      },
      "source": [
        "# View tensorboard logs of transfer learning modelling experiments (should ~4 models)\n",
        "# Upload TensorBoard dev records\n",
        "!tensorboard dev upload --logdir ./transfer_learning \\\n",
        "  --name \"Transfer Learning Experiments with 10 Food101 Classes\" \\\n",
        "  --description \"A series of different transfer learning experiments with varying amounts of data and fine-tuning.\" \\\n",
        "  --one_shot # exits the uploader once its finished uploading"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGxLBG64qpr3"
      },
      "source": [
        "My TensorBoard experiments are available at: https://tensorboard.dev/experiment/vcySzjmkRkKBLVSdAQMO8g/ \n",
        "\n",
        "If you need to view or delete a previous TensorBoard experiment, you can run the commands below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkxUAZ3UqVI3"
      },
      "source": [
        "# # View all of your uploaded TensorBoard.dev experiments (public)\n",
        "# !tensorboard dev list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LevTA0AsPB_"
      },
      "source": [
        "# # To delete an experiment\n",
        "# !tensorboard dev delete --experiment_id vcySzjmkRkKBLVSdAQMO8g # Change this for the experiment ID you want to delete"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}